Devices, systems, and methods for controlling field of view in imaging systems

ABSTRACT

Devices, systems, and methods for controlling an intravascular imaging device are provided. For example, in one embodiment a method includes communicating a control signal to an actuator of the intravascular imaging device to cause oscillation of an imaging element of the intravascular imaging device, wherein the intravascular imaging device further includes an acoustic marker; receiving imaging data from the imaging element of the intravascular imaging device; identifying the acoustic marker in the imaging data by determining a correlation between the imaging data and a template representative of the acoustic marker; adjusting an aspect of the control signal based on identifying the acoustic marker; and communicating the adjusted control signal to the actuator of the intravascular imaging device.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 13/679,719, filed Nov. 16, 2012, now U.S. Pat. No. 9,107,640,which is a continuation of U.S. patent application Ser. No. 13/665,470,filed Oct. 31, 2012, which claims priority to and the benefit of each ofU.S. Provisional Patent Application Nos. 61/553,772 and 61/553,789,filed Oct. 31, 2011, each of which is hereby incorporated by referencein its entirety.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to imagingsystems and, more particularly, to imaging systems sized for use withinhuman vasculature. In some instances, the devices, systems, and methodsof the present disclosure are directed to controlling the field of viewof such imaging systems.

BACKGROUND

In the United States and many other countries, heart disease is aleading cause of death and disability. One particular kind of heartdisease is atherosclerosis, which involves the degeneration of the wallsand lumen of the arteries throughout the body. Scientific studies havedemonstrated the thickening of an arterial wall and eventualencroachment of the tissue into the lumen as fatty material builds uponthe vessel walls. The fatty material is known as “plaque.” As the plaquebuilds up and the lumen narrows, blood flow is restricted. If the arterynarrows too much, or if a blood clot forms at an injured plaque site(lesion), flow is severely reduced, or cut off and consequently themuscle that it supports may be injured or die due to a lack of oxygen.Atherosclerosis can occur throughout the human body, but it is most lifethreatening when it involves the coronary arteries which supply oxygento the heart. If blood flow to the heart is significantly reduced or cutoff, a myocardial infarction or “heart attack” often occurs. If nottreated in sufficient time, a heart attack often leads to death.

The medical profession relies upon a wide variety of tools to treatheart disease, ranging from drugs to open heart “bypass” surgery. Often,a lesion can be diagnosed and treated with minimal intervention throughthe use of catheter-based tools that are threaded into the coronaryarteries via the femoral artery in the groin. For example, one treatmentfor lesions is a procedure known as percutaneous transluminal coronaryangioplasty (PTCA) whereby a catheter with an expandable balloon at itstip is threaded into the lesion and inflated. The underlying lesion isre-shaped, and hopefully, the lumen diameter is increased to improveblood flow. Such techniques have traditionally relied on CT scansperformed before surgery and angiograms during surgery to identifyimportant anatomical features of the vasculature associated with theinterventions. However, the information from a CT scan is ofteninaccurate at the time of surgery since the aneurysm is continuallyevolving over time.

Further, interventional procedures in the intracardiac space arecontinually developing. In that regard, structural heart procedures,including but not limited to valve replacement, valve repair, catheterablation for arrhythmia, left atrial appendage (LAA) procedures, patentforamen ovale (PFO) procedures, and atrial septal defect procedures,also rely on imaging of the corresponding heart structures. Withoutaccurate and detailed images of the associated structures, theseinterventional procedures in the intracardiac space become difficult, ifnot impossible, to perform successfully.

In recent years, techniques have been developed for obtaining detailedinformation about coronary and peripheral vessels as well as theintracardiac structures. For example, Intravascular Ultrasound (IVUS)and Intracardiac Echocardiography (ICE) techniques employ one or morevery small transducers arranged towards the end of a catheter to provideelectronically transduced echo signals to an external imaging system inorder to produce a two or three-dimensional image of the lumen, thevessel tissue, and/or the tissue surrounding the vessel. Often thesehigh quality images are generated in substantially real time. The imagesfrom these techniques allow a user to view the form and structure of asite rather then merely determining that blood is flowing.

In some instances, these devices rely on mechanical movement of animaging transducer (e.g., an ultrasound transducer) in order torepeatedly sample a multi-dimensional space. In order to provideaccurate information, effort is made to coordinate the transducer motionand the associated ultrasound acquisition. In that regard, the externalimaging system often controls the movement of the transducer. Forexample, in some instances the displacement of the imaging transducer isdirectly correlated to the voltage or current waveform of a controlsignal generated by the external imaging system.

While the existing devices and methods have been generally adequate fortheir intended purposes, they have not been entirely satisfactory in allrespects. The devices, systems, and associated methods of the presentdisclosure overcome one or more of the shortcomings of the prior art.

SUMMARY

Devices, systems, and methods for controlling the field of view inimaging systems are provided.

In one embodiment, a method of controlling an imaging device isprovided. The method comprises receiving imaging data from anoscillating imaging element of the imaging device, processing theimaging data to identify a marker associated with the imaging devicewithin the imaging data, and adjusting a control signal provided to anactuator of the imaging device. In that regard, the actuator impartsoscillating motion to the imaging element and the control signal isadjusted based on identifying the marker within the image data toachieve a desired field of view for the imaging transducer. In someinstances, the control signal is adjusted to achieve a desired end anglefor the desired field of view. Further, in some instances, the controlsignal is adjusted to achieve a desired start angle for the desiredfield of view. The oscillating imaging element is positioned within adistal portion of a flexible elongate member that sized and shaped forpositioning within an internal structure of a patient.

In some instances, the step of processing the imaging data to identifythe marker includes applying a thresholding algorithm. In that regard,applying the thresholding algorithm includes identifying points withinthe imaging data that satisfy a threshold in some implementations. Forexample, identifying points within the imaging data that satisfy thethreshold can include identifying each A-scan of a B-scan that satisfiesthe threshold. Further, a marker location within the B-scan can beselected based on the A-scans of the B-scan that satisfy the threshold.In that regard, the marker location can be selected based on a median,mean, or other characteristic of the A-scans satisfying the threshold.

In some instances, the step of processing the imaging data to identifythe marker includes applying a running-average algorithm. In thatregard, applying the running-average algorithm includes identifyingpoints within the imaging data that satisfy a threshold in someimplementations. For example, identifying points within the imaging datathat satisfy the threshold can include identifying each A-scan of aB-scan that satisfies the threshold. Further, a marker location withinthe B-scan can be selected based on a median, mean, or othercharacteristic of the A-scans of the B-scan that satisfy the threshold.

In some instances, the step of processing the imaging data to identifythe marker includes applying a correlation algorithm. In that regard,applying the correlation algorithm includes obtaining a template andcalculating a correlation between the template and a frame of theimaging data in some implementations. In that regard, the template isrepresentative of imaging data containing the marker in some instances.A marker location within the frame of the imaging data is identifiedbased on a point of maximum correlation of the frame with the templaterepresentative of imaging data containing the marker. In otherinstances, the template is representative of imaging data that does notcontain the marker. In such instances, a marker location within theframe of the imaging data is identified based on a point of minimumcorrelation of the frame with the template representative of imagingdata that does not contain the marker.

In some instances, the step of processing the imaging data to identifythe marker includes applying a sum-of-gradient algorithm. In thatregard, applying the sum-of-gradient algorithm includes calculating agradient for a portion of the imaging data and determining whether thecalculated gradient satisfies a threshold in some implementations. Insome embodiments, the method further comprises clamping image datavalues for the portion of the imaging data. In that regard, the imagedata values are clamped to the mean value of the portion of the imagingdata in some instances. In some instances, processing the imaging datato identify the marker further includes applying a sum-of-differencesalgorithm along with the sum-of-gradient algorithm. In that regard,applying the sum-of-differences algorithm includes calculatingdifferences between a first window and a second window in someinstances. In some instances, the differences between the first windowand the second window are calculated as the first window is moved acrossthe portion of the imaging data. Further, in some instances, the secondwindow is positioned halfway between the first window and a fixed pointof the portion of the imaging data. In some cases, applying thesum-of-differences algorithm also includes identifying points within theportion of the imaging data that satisfy at least one threshold. In someinstances, the identified points are those that satisfy at least twothresholds. A marker location within the portion of the imaging data isselected based on a median, mean, or other characteristic of the pointswithin the portion of the imaging data that satisfy the threshold. Inother embodiments, processing the imaging data to identify the markerfurther includes applying a sum-of-differences algorithm withoutapplying a sum-of-gradient algorithm.

In another embodiment, a method of controlling an imaging device for usewithin an internal structure of a patient is provided. The methodincludes communicating a control signal to an actuator of an imagingdevice, where the actuator causes oscillation of a movable component ofan imaging element positioned in a distal portion of the imaging devicebased on the control signal. The method also includes receiving imagingdata from the imaging element of the imaging device, processing theimaging data to identify a marker associated with the imaging devicewithin the imaging data, adjusting an aspect of the control signal basedon processing the imaging data to identify the marker; and communicatingthe adjusted control signal to the actuator of the imaging device.Processing the imaging data to identify the marker includes applying oneor more of a thresholding algorithm, a running-average algorithm, acorrelation algorithm, a sum-of-gradient algorithm, and asum-of-differences algorithm.

Additional aspects, features, and advantages of the present disclosurewill become apparent from the following detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagrammatic schematic view of a portion of an elongatedmember of an imaging system according to one aspect of the presentdisclosure.

FIG. 2 is a diagrammatic perspective view of a distal end portion of theelongated member of FIG. 1 according to an embodiment of the presentdisclosure.

FIG. 3 is a partial cross-sectional side view of a distal end portion ofthe elongated member of FIGS. 1 and 2 illustrating a transducer elementof the imaging system in a first orientation.

FIG. 4 is a partial cross-sectional side view of the distal end portionof the elongated member similar to that of FIG. 3 but illustrating thetransducer element in a second orientation.

FIG. 5 is a diagrammatic schematic view of a motion path of a transducerelement of an imaging system according to one aspect of the presentdisclosure.

FIG. 6 is a diagrammatic schematic view of the imaging scans associatedwith the motion path of FIG. 5 according to one aspect of the presentdisclosure.

FIG. 7 is a diagrammatic perspective view of a distal end portion of anelongated member that includes an imaging marker according to anembodiment of the present disclosure.

FIG. 8 is a diagrammatic cross-sectional side view of a distal endportion of an elongated member having an imaging marker according to anembodiment of the present disclosure, where a transducer of the elongatemember is in a first orientation relative to the imaging marker.

FIG. 9 is a diagrammatic cross-sectional side view of the distal endportion of the elongated member of FIG. 8, but where the transducer ofthe elongate member is in a second orientation relative to the imagingmarker.

FIG. 10 is a diagrammatic cross-sectional side view of the distal endportion of the elongated member of FIG. 8, but where the transducer ofthe elongate member is in a third orientation relative to the imagingmarker.

FIG. 11 is a diagrammatic cross-sectional side view of a distal endportion of an elongated member having an imaging marker according toanother embodiment of the present disclosure, where a transducer of theelongate member is in a first orientation relative to the imagingmarker.

FIG. 12 is a diagrammatic cross-sectional side view of the distal endportion of the elongated member of FIG. 11, but where the transducer ofthe elongate member is in a second orientation relative to the imagingmarker.

FIG. 13 is a diagrammatic cross-sectional side view of the distal endportion of the elongated member of FIG. 11, but where the transducer ofthe elongate member is in a third orientation relative to the imagingmarker.

FIG. 14 is a diagrammatic cross-sectional side view of a distal endportion of an elongated member having an imaging marker according toanother embodiment of the present disclosure, where a transducer of theelongate member is in a first orientation relative to the imagingmarker.

FIG. 15 is a diagrammatic cross-sectional side view of the distal endportion of the elongated member of FIG. 14, but where the transducer ofthe elongate member is in a second orientation relative to the imagingmarker.

FIG. 16 is a diagrammatic cross-sectional side view of the distal endportion of the elongated member of FIG. 14, but where the transducer ofthe elongate member is in a third orientation relative to the imagingmarker.

FIG. 17 is an image showing a full field of view of an imaging devicehaving an imaging marker according to an embodiment of the presentdisclosure.

FIG. 18 is a close-up of a portion of the image of FIG. 17 containingthe imaging marker.

FIG. 19 is a flow chart illustrating a method of controlling a controlsignal of an imaging system according to an embodiment of the presentdisclosure.

FIG. 20 is a diagrammatic schematic view of a portion of an imagingsystem according to an embodiment of the present disclosure configuredfor implementing one or more of the methods of controlling a field ofview of an imaging device of the present disclosure.

FIGS. 21a-21k are a series of images obtained from an imaging devicehaving a visual marker according to an embodiment of the presentdisclosure, the series of images showing the motion of the visual markeracross the field of view of the imaging device.

FIGS. 22a-22k are a series of images obtained from an imaging devicehaving a visual marker according to another embodiment of the presentdisclosure, the series of images showing the motion of the visual markeracross the field of view of the imaging device.

FIG. 23 is a graph showing the tracking of the markers of FIGS. 21a-21kand FIGS. 22a-22k according to an embodiment of the present disclosureincorporating a thresholding algorithm.

FIG. 24 is a graph showing the tracking of the markers of FIGS. 21a-21kand FIGS. 22a-22k according to another embodiment of the presentdisclosure incorporating a running average algorithm.

FIG. 25 is a flow chart illustrating a method of controlling a controlsignal of an imaging system according to an embodiment of the presentdisclosure incorporating a correlation algorithm.

FIG. 26 is an image showing identification of a region associated with avisual marker suitable for creating a template for use in a correlationalgorithm according to an embodiment of the present disclosure.

FIG. 27a is a heat map showing a cross-correlation between a template ofa correlation algorithm and a current imaging frame according to anembodiment of the present disclosure.

FIG. 27b is a close-up of a portion of the heat map of FIG. 27acontaining an area of maximum correlation.

FIG. 28 is a flow chart illustrating a method of controlling a controlsignal of an imaging system according to an embodiment of the presentdisclosure incorporating a reverse correlation algorithm.

FIG. 29 is an image showing identification of a constant signal regionand a imaging marker region that are suitable for creating a templatefor use in a reverse correlation algorithm according to an embodiment ofthe present disclosure.

FIG. 30a is a heat map showing the variance in cross-correlation valuesbetween the constant signal region and the imaging marker region of FIG.29 according to an embodiment of the present disclosure.

FIG. 30b is a line graph of the cross-correlation values of the heat mapof FIG. 30a at a particular image depth.

FIG. 31 is a heat map showing a cross-correlation between a template ofa reverse correlation algorithm and a plurality of A-scans according toan embodiment of the present disclosure.

FIG. 32 is a flow chart illustrating a method of controlling a controlsignal of an imaging system according to an embodiment of the presentdisclosure incorporating a sum-of-differences and sum-of-gradientalgorithm.

FIG. 33 is an image corresponding to limited transducer motion and agraph of the gradient of a portion of the image corresponding to thelimited transducer motion according to an embodiment of the presentdisclosure.

FIG. 34 is an image corresponding to full-range transducer motion and agraph of the gradient of a portion of the image corresponding to thefull-range transducer motion according to an embodiment of the presentdisclosure.

FIG. 35 is a line graph of the sum-of-gradient across a plurality offrames according to an embodiment of the present disclosure.

FIG. 36 includes a first (upper) heat map illustrating detection of aimaging marker across a plurality of A-scans before clamping and asecond (lower) heat map illustrating detection of the imaging markeracross the plurality of A-scans after clamping according to anembodiment of the present disclosure.

FIG. 37 is an image showing identification of a first window and asecond window utilized in a sum-of-differences algorithm according to anembodiment of the present disclosure.

FIG. 38 is a heat map showing detection of an imaging marker across aplurality of frames of a dataset according to an embodiment of thepresent disclosure.

FIG. 39 is a flow chart illustrating a method of controlling a controlsignal of an imaging system according to an embodiment of the presentdisclosure incorporating a sum-of-differences and sum-of-gradientalgorithm.

FIG. 40 is a diagrammatic schematic view of a portion of an imagingsystem illustrating aspects of a sum-of-differences and sum-of-gradientalgorithm according to an embodiment of the present disclosure.

FIG. 41 is a timeline illustrating a timing of processing steps of asum-of-differences and sum-of-gradient algorithm according to anembodiment of the present disclosure.

FIG. 42 is a diagrammatic schematic view of a hardware architecturesuitable for implementing a sum-of-gradient portion of an algorithmaccording to an embodiment of the present disclosure.

FIG. 43 is a diagrammatic schematic view of a hardware architecturesuitable for implementing a sum-of-differences portion of an algorithmaccording to an embodiment of the present disclosure.

FIG. 44 is a diagrammatic schematic view of a hardware architecturesuitable for implementing a sum-of-differences portion of an algorithmaccording to another embodiment of the present disclosure.

FIG. 45 is a diagrammatic schematic view of a hardware architecturesuitable for determining a location of a visual marker based on a medianvalue according to an embodiment of the present disclosure.

FIG. 46 is a diagrammatic schematic view of a hardware architecturesuitable for acting as a control loop for a sum-of-differences andsum-of-gradient algorithm according to an embodiment of the presentdisclosure.

FIG. 47 is a diagrammatic schematic view of an imaging field of viewillustrating aspects of a point of symmetry detection algorithmaccording to an embodiment of the present disclosure.

FIG. 48 is a diagrammatic schematic view of an imaging field of viewillustrating a template region of a point of symmetry detectionalgorithm according to an embodiment of the present disclosure.

FIG. 49 is a diagrammatic schematic view of the imaging field of view ofFIG. 48 illustrating a mirror region adjacent to the template region ofthe point of symmetry detection algorithm according to an embodiment ofthe present disclosure.

FIG. 50 is a diagrammatic schematic view of the imaging field of view ofFIGS. 48 and 49 illustrating a further template region of the point ofsymmetry detection algorithm according to an embodiment of the presentdisclosure.

FIG. 51 is a diagrammatic schematic view of the imaging field of view ofFIGS. 48-50 illustrating a further mirror region adjacent to the furthertemplate region of the point of symmetry detection algorithm accordingto an embodiment of the present disclosure.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of thepresent disclosure, reference will now be made to the embodimentsillustrated in the drawings, and specific language will be used todescribe the same. It is nevertheless understood that no limitation tothe scope of the disclosure is intended. Any alterations and furthermodifications to the described devices, systems, and methods, and anyfurther application of the principles of the present disclosure arefully contemplated and included within the present disclosure as wouldnormally occur to one skilled in the art to which the disclosurerelates. In particular, it is fully contemplated that the features,components, and/or steps described with respect to one embodiment may becombined with the features, components, and/or steps described withrespect to other embodiments of the present disclosure. For the sake ofbrevity, however, the numerous iterations of these combinations will notbe described separately.

Referring to FIGS. 1-4, shown therein are aspects of an elongated member100 of an imaging system according to an embodiment of the presentdisclosure. More specifically, FIG. 1 is a diagrammatic schematic viewof a portion of the elongated member 100; FIG. 2 is a diagrammaticperspective view of a distal end portion of the elongated member 100;FIG. 3 is a partial cross-sectional side view of a distal end portion ofthe elongated member 100, illustrating a transducer element of theimaging system in a first orientation; FIG. 4 is a partialcross-sectional side view of the distal end portion of the elongatedmember 100, similar to that of FIG. 3, but illustrating the transducerelement in a second orientation.

As shown in FIG. 1, the elongated member 100 includes a flexible body102 having a distal housing portion 104 extending to a distal tip 106.As shown in FIGS. 2-4, a transducer 108 is disposed within the distalhousing portion 104 adjacent the distal tip 106. In some instances thetransducer 108 is an ultrasound transducer. In the illustratedembodiment, the transducer 108 is mounted on a platform 110 that isconfigured to rotate about an axis defined by a pivot pin 112 extendingthrough a portion of the platform 110. In that regard, transducer 108rotates—in the direction of arrow 114—from an initial orientation (shownin FIG. 3) to a fully-rotated orientation (shown in FIG. 4). From thefully-rotated orientation, the transducer rotates—in the directionopposite of arrow 114—back to the initial orientation. This process isrepeated to cause oscillation of the transducer 108.

In the illustrated embodiment, an interface arm 116 extends proximallyfrom the platform 110 and interfaces with an actuator 118 to facilitateoscillation of the transducer 108. In that regard, in some instances theactuator 118 is a shape-memory actuator as described in U.S. Pat. No.7,658,715, titled “MINIATURE ACTUATOR MECHANISM FOR INTRAVASCULARIMAGING,” or U.S. Provisional Patent Application No. 61/546,419, filedon Oct. 12, 2011, each hereby incorporated by reference in its entirety.In other instances, the actuator 118 is driven by a drive shaft or otherphysical connection to a motor or other driving device positionedadjacent a proximal portion of the elongated member. In yet otherinstances, the actuator is driven by hydraulics, air pressure, and/orother means of transmitting mechanical energy into motion. As a generalmatter, the actuator 118 can be any type of device suitable forimparting sweeping, oscillatory, and/or rotational movement to thetransducer 108. As shown in FIG. 3, when the transducer 108 is in theinitial position advancement of the actuator 118 distally, as indicatedby arrow 120, urges the interface arm 116 distally, which causes theplatform 110 to rotate about the pivot pin 112. Rotation of the platform110 sweeps the transducer 108 from the initial position (FIG. 3) to thefully-rotated position (FIG. 4).

In that regard, FIG. 5 illustrates an exemplary motion profile of thetransducer 108. As shown, the transducer 108 pivots about the pivot pin112 and travels across an angle 124 between a starting orientation(represented by axis 126 and the transducer 108 shown in phantom on thefar left of the drawing) and an ending orientation (represented by axis128 and the transducer 108 shown on the far right of the drawing). Theangle 124 that the transducer 108 travels between the startingorientation and the ending orientation is generally between about 1degree and about 400 degrees, depending on the imaging application. Insome instances, the angle 124 is between about 25 degrees and about 360degrees. In some particular instances, the angle 124 is approximately120 degrees. It is understood, however, that the present disclosure isapplicable to any amount of transducer rotation and no limitation isintended by these exemplary ranges.

Referring now to FIG. 6, shown therein is a diagrammatic schematicrepresentation of the imaging scans associated with the motion path ofimaging transducer 108 shown in FIG. 5. In that regard, in order tobetter understand the techniques of the present disclosure, it isnecessary to first understand the image format nomenclature that will beused herein to describe various embodiments of the present disclosure.In that regard, typically as the ultrasound transducer or opticalelement traverses through its motion profile, it collects data on aline-by-line basis as the transducer is repeatedly transitioned betweensend and receive modes. Each line is generally referred to as an“A-Scan” and it contains data sampled at defined depths. Once thetransducer has travelled the entire field-of-view, the set of A-Scansare collected and grouped together as a “B-Scan” or “frame.”Accordingly, a “B-Scan” or “frame” is most commonly understood to be theset of “A-Scans” or lines associated with the transducer or otherimaging element traveling once along its motion profile. In that regard,each of the lines in FIG. 6 is representative of an “A-scan,” whereasthe entire collection of lines in FIG. 6 is representative of a“B-scan.” It should be noted, however, that in some embodiments of thepresent disclosure, for various reasons, one or more A-scans are omittedfrom the collection of A-scans that are grouped together to form aB-scan.

While the transducer 108 has been described as under going oscillatorymotion, in other instances the transducer 108 is maintained in a fixedposition and a mirror or other reflective element is oscillated. In thatregard, the mirror or other reflective element reflects the signalsgenerated by the fixed transducer (e.g., acoustic waves associated withultrasound imaging) such that the signals are swept through the motionprofile in a manner similar to when the transducer itself is oscillated.In some instances, the fixed transducer and reflector are configured ina manner similar to the embodiments described U.S. Pat. No. 7,658,715,titled “MINIATURE ACTUATOR MECHANISM FOR INTRAVASCULAR IMAGING,” whichis hereby incorporated by reference in its entirety.

In general, the elongate member 100 is sized and shaped for use withinan internal structure of a patient, including but not limited to apatient's arteries, veins, heart chambers, neurovascular structures, GItrack, bronchials, organs, and/or other areas where internal imaging ofpatient anatomy is desirable. In that regard, depending on theparticular medical application, the elongate member 100 is configuredfor use in cardiology procedures, neurovascular procedures, pulmonologyprocedures, endoscopy procedures, colonoscopy procedures, naturalorifice procedures (such as Natural Orifice Translumenal EndoscopicSurgery (NOTES)), and/or other medical procedures.

Accordingly, in some embodiments the elongate member 100 takes the formof a guidewire or catheter. In some instances, the imaging system as awhole, the elongate member 100, the actuator 118, and/or other aspectsof the imaging system are similar to those described in U.S. Pat. No.5,379,772, titled “FLEXIBLE ELONGATE DEVICE HAVING FORWARD LOOKINGULTRASONIC IMAGING,” U.S. Pat. No. 7,115,092, titled “TUBULAR COMPLIANTMECHANISMS FOR ULTRASONIC IMAGING SYSTEMS AND INTRAVASCULARINTERVENTIONAL DEVICES,” and/or U.S. Pat. No. 7,658,715, titled“MINIATURE ACTUATOR MECHANISM FOR INTRAVASCULAR IMAGING,” each of whichis hereby incorporated by reference in its entirety.

To function most effectively, the data acquired with the transducer 108must be coordinated with the transducer's motion. Accordingly, in someaspects, the present disclosure is directed to control mechanisms thatmonitor and control the motion of the transducer and, thereby, controlthe resulting field of view of the imaging system. In that regard,aspects of the present disclosure increase the accuracy andreproducibility of the transducer's motion. This results in improvedclarity and accuracy in the resulting images provided by the imagingsystems. In that regard, some embodiments of the field-of-view controltechniques of the present disclosure are suitable for use in the contextof one or more acoustic or imaging markers or targets. Further, in someparticular instances, imaging devices of the present disclosure includeone or more acoustic or other imaging-modality markers that are utilizedby the field-of-view control techniques of the present disclosure. A fewexemplary embodiments of imaging devices having such markers aredescribed below, but no limitation is intended thereby. Rather, it isunderstood that the field-of-view control techniques of the presentdisclosure are suitable for use with virtually any type of marker,including markers of various shapes, sizes, and materials, markerspositioned in, on, adjacent to, and spaced from an imaging device,and/or markers otherwise identifiable by an imaging system.

As noted, in some embodiments a marker is utilized to monitor theposition of the transducer 108 during its motion profile. In thatregard, the marker facilitates detection of when/if the transducer 108reaches a certain point along its motion profile. For example, in someinstances the marker is configured such that it is detected when thetransducer 108 reaches the ending orientation, as represented by axis128. In other instances, the marker is configured to be detected ifand/or when the transducer 108 reaches other points along its motionprofile, including but not limited to the starting orientation, amid-point orientation (represented by transducer 108 shown in phantom inthe middle of the drawing of FIG. 5), and/or other orientations alongthe motion profile. In that regard, the boundaries of the motion profileof the transducer 108 are illustrated in FIG. 5 by axes 126 and 128.These boundaries are representative of the desired motion profile of thetransducer during use. However, it is understood that the actual motionprofile of the transducer may vary during use and, therefore, may travelbeyond the boundaries of the desired motion profile. Accordingly, insome instances, the marker is configured to be detected if and/or whenthe transducer 108 reaches a point beyond the desired motion profile.Further, in some embodiments, markers are utilized to detect if and/orwhen the transducer 108 reaches two or more points along the motionprofile, rather than a single point. In that regard, in some instancesthe two or more points are spaced apart by a fixed distance and/or anglefrom one another.

Exemplary embodiments of configurations of imaging devices havingmarkers suitable for use with the techniques of the present disclosurewill now be described in the context of FIGS. 7-16. For the sake ofclarity and simplicity, the discussion herein will use the endingorientation of the transducer (represented by axis 128 and thetransducer 108 shown on the far right of FIG. 5) as the location of thedescribed markers. However, no limitation is intended thereby. Rather,it is explicitly understood that the one or more of the describedmarkers and/or other types of markers may be positioned for detection atone or more points along the motion profile of the transducer asdiscussed above.

Referring to FIGS. 7-16, shown therein are several exemplary embodimentsof imaging devices incorporating markers that are suitable for use withthe field-of-view control techniques of the present disclosure. In thatregard, referring more specifically to FIG. 7, shown therein is a distalportion of an elongated member 150 according to an embodiment of thepresent disclosure. The distal portion of the elongated member 150includes a housing 154 extending to a distal tip 156. Further, anultrasound transducer 158 is positioned within the distal portion of theelongated member 150 and pivots about a pivot pin 160. The elongatedmember 150 also includes an acoustic target 162 positioned between arms164 and 166. In the illustrated embodiment, the acoustic target 162 ispositioned such that when the ultrasound transducer 158 reaches theending orientation of its motion profile the acoustic target 162 iswithin the visible field of the ultrasound transducer 158. In thatregard, when the acoustic target 162 is within the visible field orframe of the ultrasound transducer 158 the acoustic target 162 isidentifiable as an acoustic signal. The acoustic target 162 is formedfrom a material having a high (or low) acoustic reflectivity relative toits environment during use. In some instances, the acoustic target 162has an easily recognizable shape, such as a simple geometrical profile.Accordingly, a processing system receiving data from the ultrasoundtransducer 158 can determine whether the acoustic target 162 is presentin any particular image or set of images. Various techniques for makingthis determination are discussed below in detail. In this manner, thesystem can recognize when the ultrasound transducer 158 reaches the fullfield of view.

Further, while the acoustic target 162 has been described in the contextof an ultrasound transducer 158, it is understood that a similar conceptmay be employed with an optical or optoelectronic sensor. In thatregard, instead of an acoustic target, a visual target that isidentifiable by the optical sensor may be utilized. Further still, insome instances a plurality of acoustic targets 162 are utilized atdiscrete points along the motion profile of the transducer, includingbut not limited to the starting orientation, the mid-point orientation,the ending orientation, and/or points in between. These variations arelikewise applicable to the other embodiments described herein.

Referring now to FIGS. 8-10, shown therein are aspects of an imagingdevice 170 according to an embodiment of the present disclosure.Referring initially to FIG. 8, a diagrammatic cross-sectional side viewof a distal portion 172 of the imaging device 170 is shown. In thatregard, the imaging device 170 includes an imaging element 174 thatrotates about a pivot axis 176 in response to actuator 178. The imagingelement 174 may be any type of imaging element (e.g., ultrasoundtransducer, optical emitter/receiver, reflector, and/or otherwise), butfor the sake of clarity will be described as an ultrasound transducer.In the illustrated embodiment, the imaging element 174 pivots about thepivot axis 176 through a motion profile having an angle between about100 degrees and about 160 degrees in some instances. In otherembodiments, the imaging element 174 pivots about the pivot axis througha motion profile having an angle less than about 100 degrees or morethan about 160 degrees.

Generally, for a particular imaging device or a specific application ofthat imaging device there is a particular motion profile angle that isdesirable. It is understood that this value varies greatly betweendevices and even application of a single device. For example, as notedabove the desired motion profile angle may be between about 1 degree andabout 400 degrees. For the sake of clarity in understanding the conceptsrelated to marker discussed with respect to the embodiment of FIGS. 8-10(and the embodiments of FIGS. 11-16 discussed below), a desired motionprofile angle of 120 degrees will be assumed. More specifically,referring to FIG. 8, the desired motion profile will be assumed to befrom 60 degrees to either side of the longitudinal axis 180 of theimaging device 170. That is, the desired motion profile will have adesired starting orientation where the transducer 174 is positioned at a60 degree angle to the left of the longitudinal axis 180 as viewed inFIG. 8 and a desired ending orientation where the transducer 174 ispositioned at a 60 degree angle to the right of the longitudinal axis asviewed in FIG. 8. For reference, the orientation of the transducer 174illustrated in FIG. 8 is a 50 degree angle to the right of thelongitudinal axis 180. However, no limitation is intended by thispresumed motion profile as the concepts of the present disclosure areequally applicable to other motion profile angles, including smallerangles, larger angles, varying angles, angle ranges, and/or othervariations of the motion profile.

As shown in FIG. 8, the imaging device 170 includes a marker or target182. In some instances, the marker 182 is positioned between arms of theimaging device similar to acoustic target 162 of elongated member 150discussed above. In the illustrated embodiment, the marker 182 ispositioned such that when the ultrasound transducer 174 reaches theending orientation of its motion profile the marker 182 is within thevisible field of the transducer 174. In that regard, with the markerwithin the visible field or frame of the transducer 174, the marker 182can be identified within the imaging data obtained from the transducer.Accordingly, a processing system receiving data from the transducer 174can determine whether the marker 182 is present in any particular imageor set of images and/or the location of the marker within such an imageor set of images.

In the embodiment of FIG. 8, the marker 182 is a wall or portion of awall defined by the distal portion 172 of the imaging device. The marker182 extends along axis 183 that is parallel to the longitudinal axis 180in the illustrated embodiment. In some instances, an inner wall 184defined by the marker 182 is planar. In other instances, the inner wall184 is at least partially concave. In yet other instances, the innerwall 184 is at least partially convex. In some instances, the inner wall184 is a combination of two or more of planar, concave, and/or convexsurfaces. In that regard, the inner wall 184 is sized and shaped toreflect energy emitted from the transducer 174 when the transducerreaches one or more points along the motion profile of the transducer.For example, in the embodiment of FIG. 8, the marker 182 is configuredto reflect energy emitted from the transducer 174 when the transducerreaches the ending orientation (i.e., 60 degrees offset to the right ofthe longitudinal axis 180 as viewed in FIG. 8). To that end, in theillustrated embodiment a distal most portion or distal tip 186 of themarker 182 ends at position that is in alignment with the axis ofrotation 176. In that regard, the distal tip 186 of the marker 182 endsat an axis 188 extending perpendicular to the longitudinal axis 180 andthrough the pivot axis 176, as shown. However, in other embodiments thedistal tip 186 of the marker 182 ends at positions proximal to the pivotaxis 176 (downward as viewed in FIG. 8) and positions distal to thepivot axis 176 (upward as viewed in FIG. 8). The positioning of thedistal tip 186 of the marker 182 along the longitudinal axis 180relative to the pivot axis 176 is selected based on the desiredfield-of-view angle in some instances.

In some instances, the marker 182 is integrally formed with theremaining portions of the housing or distal portion 172. For example, insome instances the distal portion 172 is machined from a single piece ofmaterial to include the marker 182. In other instances, the marker 182is fixedly secured to the distal portion 172 via welding, adhesive,bonding, mechanical connection, and/or combinations thereof. In thatregard, the marker 182 is formed of the same material as other portionsof the housing or distal portion 172 in some embodiments. However, inother embodiments the marker 182 is formed of a material different thanthe remaining portions of the housing or distal portion 172. Examples ofsuitable materials for the marker 182 and/or the distal portion 172include without limitation, biocompatible metals (e.g., stainless steel,cobalt chrome, etc.), ceramics, and polymers.

As noted above, in the embodiment of FIGS. 8-10, the marker 182 isconfigured to reflect energy emitted from the transducer 174 when thetransducer reaches the ending orientation of the transducer motionprofile (i.e., 60 degrees offset to the right of the longitudinal axis180 as viewed in FIG. 8). In that regard, referring to FIG. 8, when thetransducer 174 is at a 50 degree angle to the right of the longitudinalaxis 180, 10 degrees short of the ending orientation, none (or anegligible amount) of the energy emitted by the transducer is reflectedby the marker 182 and back onto the transducer. However, referring toFIG. 9, when the transducer 174 is at a 60 degree angle to the right ofthe longitudinal axis 180 (i.e., at the ending orientation) a portion190 of the energy emitted by the transducer contacts the marker 182 andreflects a signal 192 back onto the transducer. Referring now to FIG.10, when the transducer 174 is at a 70 degree angle to the right of thelongitudinal axis 180 (i.e., 10 degrees beyond the ending orientation) aportion 194 of the energy emitted by the transducer contacts the marker182 and reflects a signal 196 back onto the transducer. As shown, boththe emitted portion 194 of the signal sent from the transducer thatcontacts the marker and the reflected signal 196 that is received by thetransducer from the marker in the over-driven position of FIG. 10 areincreased relative to the emitted portion 190 and reflected signal 192associated with the ending orientation of FIG. 9. In some instances, asthe signal strength increases the ability to identify the marker in theresulting image data becomes easier. Further, in some embodiments therelative strength and/or area of the reflected signal can be utilized todetermine an orientation of the transducer.

Referring now to FIGS. 11-13, shown therein are aspects of an imagingdevice 200 according to an embodiment of the present disclosure.Referring initially to FIG. 11, a diagrammatic cross-sectional side viewof a distal portion 202 of the imaging device 200 is shown. In thatregard, the imaging device 200 includes some elements similar to thosediscussed above with respect to imaging device 170 and, therefore, thesame reference numerals are utilized in the context of imaging device200. For example, the imaging device 200 includes an imaging element 174that rotates about a pivot axis 176 in response to actuator 178. Theimaging element 174 may be any type of imaging element (e.g., ultrasoundtransducer, optical emitter/receiver, reflector, and/or otherwise), butfor the sake of clarity will be described as an ultrasound transducer.

As shown in FIG. 11, the imaging device 200 includes a marker or target204. In some instances, the marker 204 is positioned between arms of theimaging device similar to acoustic target 162 of elongated member 150discussed above. In the illustrated embodiment, the marker 204 ispositioned such that when the ultrasound transducer 174 reaches theending orientation of its motion profile the marker 204 is within thevisible field of the transducer 174. In that regard, with the marker 204within the visible field or frame of the transducer 174, the marker 204can be identified within the imaging data obtained from the transducer.Accordingly, a processing system receiving data from the transducer 174can determine whether the marker 204 is present in any particular imageor set of images and/or the location of the marker within such an imageor set of images.

In the embodiment of FIG. 11, the marker 204 is a wall or portion of awall defined by the distal portion 202 of the imaging device 200. Themarker 204 has an inner wall 206 that extends at an oblique angle withrespect to the longitudinal axis 180 and the axis 188 of the imagingdevice 200. In that regard, in some embodiments the inner wall 206extends at an angle 207 between about 1 degree and about 89 degreesrelative to an axis extending perpendicular to the longitudinal axis 180(e.g., axis 188), which corresponds to an angle between about 89 degreesand about 1 degree relative to the longitudinal axis 180. In someparticular embodiments, the inner wall extends at an angle between about50 degrees and about 80 degrees, which corresponds to an angle betweenabout 40 degrees and about 10 degrees relative to the longitudinal axis180. In some instances, the angle 207 is selected such that the innerwall extends substantially perpendicular to the primary axis oftransmission of the imaging element (e.g., the ultrasound acousticvector) when the desired field of view is reached. In the illustratedembodiment of FIG. 11, the inner wall 206 extends at approximately a 60degree angle relative to the axis 188. In some instances, the inner wall206 defined by the marker 204 is planar. In other instances, the innerwall 206 is at least partially concave. In yet other instances, theinner wall 206 is at least partially convex. In some instances, theinner wall 206 is a combination of two or more of planar, concave,and/or convex surfaces. A benefit of the angled marker 206 is that themarker 206 is more easily visible or identifiable compared to when themarker is not angled because of the increased signal reflectionresulting from the reflection angle of the transducer signal.

The inner wall 206 is sized and shaped to reflect energy emitted fromthe transducer 174 when the transducer reaches one or more points alongthe motion profile of the transducer. For example, in the embodiment ofFIG. 11, the marker 204 is configured to reflect energy emitted from thetransducer 174 when the transducer reaches the ending orientation (i.e.,60 degrees offset to the right of the longitudinal axis 180 as viewed inFIG. 11). More specifically, the inner wall 206 is configured to extendsubstantially parallel to the outer surface of the transducer 174 whenthe transducer is in the ending orientation such that the inner wall 206extends substantially perpendicular to a primary axis along which thetransducer 174 will emit energy. Further, in the illustrated embodimenta distal most portion or distal tip 208 of the marker 204 ends atposition that is in alignment with the axis of rotation 176 of thetransducer 174. In that regard, the distal tip 208 of the marker 204ends at an axis 188 extending perpendicular to the longitudinal axis 180and through the pivot axis 176, as shown. However, in other embodimentsthe distal tip 208 of the marker 204 ends at positions proximal to thepivot axis 176 (downward as viewed in FIG. 11) and positions distal tothe pivot axis 176 (upward as viewed in FIG. 11). The positioning of thedistal tip 208 of the marker 204 along the longitudinal axis 180relative to the pivot axis 176 is selected based on the desiredfield-of-view angle in some instances.

As noted above, in the embodiment of FIGS. 11-13, the marker 204 isconfigured to reflect energy emitted from the transducer 174 when thetransducer reaches the ending orientation of the transducer motionprofile (i.e., 60 degrees offset to the right of the longitudinal axis180 as viewed in FIG. 11). In that regard, referring to FIG. 11, whenthe transducer 174 is at a 50 degree angle to the right of thelongitudinal axis 180, 10 degrees short of the ending orientation, none(or a negligible amount) of the energy emitted by the transducer isreflected by the marker 204 and back onto the transducer. However,referring to FIG. 12, when the transducer 174 is at a 60 degree angle tothe right of the longitudinal axis 180 (i.e., at the ending orientation)a portion 210 of the energy emitted by the transducer contacts themarker 204 and reflects a signal back onto the transducer. In thatregard, the inner surface 206 of the marker 204 is substantiallyperpendicular to the outer surface of the transducer 174. Referring nowto FIG. 13, when the transducer 174 is at a 70 degree angle to the rightof the longitudinal axis 180 (i.e., 10 degrees beyond the endingorientation) a portion 214 of the energy emitted by the transducercontacts the marker 204 and reflects a signal 216 back onto thetransducer.

Referring now to FIGS. 14-16, shown therein are aspects of an imagingdevice 220 according to an embodiment of the present disclosure.Referring initially to FIG. 14, a diagrammatic cross-sectional side viewof a distal portion 222 of the imaging device 220 is shown. In thatregard, the imaging device 220 includes some elements similar to thosediscussed above with respect to imaging devices 170 and 200 and,therefore, the same reference numerals are utilized in the context ofimaging device 220. For example, the imaging device 220 includes animaging element 174 that rotates about a pivot axis 176 in response toactuator 178. The imaging element 174 may be any type of imaging element(e.g., ultrasound transducer, optical emitter/receiver, reflector,and/or otherwise), but for the sake of clarity will be described as anultrasound transducer.

As shown in FIG. 14, the imaging device 220 includes a marker or target224. In some instances, the marker 224 is positioned between arms of theimaging device similar to acoustic target 162 of elongated member 150discussed above. In the illustrated embodiment, the marker 224 ispositioned such that when the ultrasound transducer 174 reaches theending orientation of its motion profile the marker 224 is within thevisible field of the transducer 174. In that regard, with the marker 224within the visible field or frame of the transducer 174, the marker 224can be identified within the imaging data obtained from the transducer.Accordingly, a processing system receiving data from the transducer 174can determine whether the marker 224 is present in any particular imageor set of images and/or the location of the marker within such an imageor set of images.

In the embodiment of FIG. 14, the marker 224 is a wall or portion of awall defined by the distal portion 222 of the imaging device 220. Themarker 224 has an inner wall 226 that extends at an oblique angle withrespect to the longitudinal axis 180 and the axis 188 of the imagingdevice 200. In that regard, in some embodiments the inner wall 226extends at an angle 227 between about 1 degree and about 89 degreesrelative to an axis extending perpendicular to the longitudinal axis 180(e.g., axis 188), which corresponds to an angle between about 89 degreesand about 1 degree relative to the longitudinal axis 180. In someparticular embodiments, the inner wall extends at an angle between about50 degrees and about 80 degrees, which corresponds to an angle betweenabout 40 degrees and about 10 degrees relative to the longitudinal axis180. In some instances, the angle 227 is selected such that the innerwall extends substantially perpendicular to the primary axis oftransmission of the imaging element (e.g., the ultrasound acousticvector) when the desired field of view is reached. In the illustratedembodiment of FIG. 14, the inner wall 226 extends at a 60 degree anglerelative to the axis 188. In some instances, the inner wall 226 definedby the marker 224 is planar. In other instances, the inner wall 226 isat least partially concave. In yet other instances, the inner wall 226is at least partially convex. In some instances, the inner wall 226 is acombination of two or more of planar, concave, and/or convex surfaces.

The inner wall 226 is sized and shaped to reflect energy emitted fromthe transducer 174 when the transducer reaches one or more points alongthe motion profile of the transducer. For example, in the embodiment ofFIG. 14, the marker 224 is configured to reflect energy emitted from thetransducer 174 when the transducer reaches the ending orientation (i.e.,60 degrees offset to the right of the longitudinal axis 180 as viewed inFIG. 14). More specifically, the inner wall 226 is configured to extendsubstantially parallel to the outer surface of the transducer 174 whenthe transducer is in the ending orientation such that the inner wall 226extends substantially perpendicular to a primary axis along which thetransducer 174 will emit energy. Further, in the illustrated embodimenta distal most portion or distal tip 228 of the marker 224 ends atposition that is distal of the axis of rotation 176 of the transducer174. In that regard, the distal tip 228 of the marker 224 ends atdistance 230 between about 0.001 inches and about 0.100 inches distal ofan axis 188 extending perpendicular to the longitudinal axis 180 andthrough the pivot axis 176, as shown.

In some instances, the distal tip 228 of the marker 224 is positionedsuch that a distal boundary of the inner surface 226 (e.g., where thedistal tip 228 and the inner surface 226 come together) is at apredetermined angle 231 with respect to the axis 188. For example, inthe illustrated embodiment the distal tip 228 is positioned such thatthe distal boundary of the inner surface 226 is at approximately a 15degree angle with respect to the axis 188 as measured from the pointwhere the central longitudinal axis 180 intersects the axis 188.Generally, the angle as measured from the point where the centrallongitudinal axis 180 intersects the axis 188 is between about 1 degreeand about 89 degrees and, in some particular instances, is between about1 degree and about 75 degrees. In some instances, instead of extendingsubstantially perpendicular to the longitudinal axis 180 (as shown inFIG. 14), the surface defining the distal tip 228 of the marker extendsan oblique angle with respect to the longitudinal axis. In someinstances, the surface defining the distal tip 228 extends at an anglebetween about 1 degree and about 89 degrees and, in some particularinstances, extends at an angle between about 1 degree and about 75degrees. The distance the distal tip 228 of the marker 224 extends alongthe longitudinal axis 180 beyond the pivot axis 176 and/or the angle ofthe surface defining the distal tip 228 are selected based on thedesired field-of-view angle in some instances.

As noted above, in the embodiment of Figs.14-16, the marker 224 isconfigured to reflect energy emitted from the transducer 174 when thetransducer reaches the ending orientation of the transducer motionprofile (i.e., 60 degrees offset to the right of the longitudinal axis180 as viewed in FIG. 14). In that regard, referring to FIG. 14, whenthe transducer 174 is at a 50 degree angle to the right of thelongitudinal axis 180, 10 degrees short of the ending orientation, none(or a negligible amount) of the energy emitted by the transducer isreflected by the marker 204 and back onto the transducer. However,referring to FIG. 15, when the transducer 174 is at a 60 degree angle tothe right of the longitudinal axis 180 (i.e., at the ending orientation)a portion 232 of the energy emitted by the transducer contacts themarker 224 and reflects a signal 234 back onto the transducer. Referringnow to FIG. 16, when the transducer 174 is at a 70 degree angle to theright of the longitudinal axis 180 (i.e., 10 degrees beyond the endingorientation) a portion 236 of the energy emitted by the transducercontacts the marker 224 and reflects a signal 238 back onto thetransducer.

As the discussed above, markers, such as markers 162, 182, 204, and 224,are incorporated into the distal portions of the imaging devices in someinstances, the marker is often positioned between about 0.051 mm (about0.002 inches) and about 5 mm (about 0.197 inches) from the longitudinalaxis of the imaging device. As a result, in some instances the marker ispositioned between about 0.0254 mm (about 0.001″) and about 1 mm (about0.0394 inches) from the transducer 174 when the transducer is aimed atthe marker such that energy is reflected back towards the transducerfrom the marker. In contrast, the depth of focus of the transducer orother imaging element used for imaging the anatomy or other structure istypically much larger, for example between about 3 mm and about 15 mm insome instances. In that regard, FIG. 17 shows a full field of viewscreen shot 250 of an imaging device having an imaging marker accordingto an embodiment of the present disclosure, while FIG. 18 shows aclose-up of a portion 252 of the image of FIG. 17 (as indicated by thedotted-line box) containing the imaging marker. As shown FIGS. 17 and18, the marker 254 is positioned at a very shallow depth of focusrelative to the overall depth of the focus of the imaging device. As aresult, the image data at depths where the marker 254 is found is oftenvery noisy. For this reason, in some embodiments this near-field regionof the image data is omitted from the display that is provided to auser. However, it is still necessary to identify the marker 254 withinthe image data. Various techniques suitable for identifying the marker254 within the noisy signal of the close depth of focus and controllingthe field of view of the imaging device based thereon are discussedbelow.

The imaging targets or markers and the associated field-of-view controltechniques of the present disclosure are suitable for use in a widevariety of catheters, guidewires, and other elongate imaging deviceshaving medical applications. In that regard, the targets areincorporated into imaging devices having forward looking and/orside-looking capabilities in some instances. That is, the targets areincorporated into imaging devices that are configured to image generallyalong the longitudinal axis of the imaging device (i.e.,forward-looking) and/or generally perpendicular to the longitudinal axisof the imaging device (i.e., side-looking). Further, in some instancesthe targets are incorporated into imaging devices that are configured toimage at an oblique angle (either distally or proximally) relative tothe longitudinal axis of the imaging device. As a result, thefield-of-view control techniques of the present disclosure are likewiseutilized in the context of forward-looking, side-looking, andoblique-looking imaging devices.

Combinations of one or more of the embodiments of targets and/orfield-of-view control techniques described herein can also be used. Thesmall size and relative simplicity of the structures of the markers ofthe present disclosure make it possible to manufacture imaging devicesthat utilize two or more markers within a single catheter, guidewire, orother imaging device, including devices ranging from about 0.5 Fr (0.16mm, 0.006 inches) up to 12 Fr (4 mm, 0.1 inches) or larger in outsidediameter or cross-sectional width. For example, in some particularembodiments the feedback mechanisms of the present disclosure areincorporated into guidewires having a diameter of 0.011 inches, 0.014inches, and/or 0.018 inches.

Referring now to FIG. 19, shown therein is a flow chart illustrating amethod 270 of controlling a control signal of an imaging systemaccording to an embodiment of the present disclosure. The method 270provides a technique that allows a detectable marker, such as thosedescribed above, to be utilized to control the field of view of animaging device. In that regard, method 270 utilizes the data obtainedusing one or more markers to provide a feedback control loop for moreconsistent scanning and accurate imaging. The method 270 begins at step272 with the imaging system providing a baseline control signal to theimaging device, such as the elongated members discussed above. At step274, signals are received from the imaging device. In some instances,the signals include the imaging data received from the imaging device.At step 276, the signals are processed to determine if any adjustmentsto the control signal are necessary. In some instances, this processingstep includes detecting and identifying the marker(s) within the imagingdata to determine a motion profile of the transducer, which correspondsto the field of view of the imaging device. Various techniques fordetecting and identifying markers from image data are discussed below.In that regard, one or more of the techniques discussed below areimplemented as part of step 276 of method 270 in some instances. Basedon the motion profile and/or field of view, it is determined whether anyadjustments to the control signal are necessary. If no adjustments arenecessary, then the method 270 continues to step 278 where the previouscontrol signal is output to the imaging device again. However, ifadjustments to the control signal are necessary, then the method 270continues to step 280 where the control signal is adjusted based on thesignals received from the imaging device at step 274. With theappropriate correction to the control signal calculated at step 280, anadjusted control signal is output to the imaging device at step 282.Then the method 270 continues at step 274 where the feedback signalsbased on the adjusted control signal are received. This iterativeprocess continues during the operation of the imaging system to providea consistent transducer motion profile that, in turn, provides accurateimaging.

Referring now to FIG. 20, shown therein is a diagrammatic schematic viewof a portion of an imaging system 320 according to an embodiment of thepresent disclosure. In that regard, the illustrated portion of theimaging system 320 is configured for implementing one or more of thefield-of-view control methods of the present disclosure. As shown, theimaging system 320 includes a controller 322 and an imaging device 324.The controller 322 has a control signal output 326 that sends a controlsignal to the imaging device 324. The controller 322 also receivessignals from the imaging device 324 via an input unit 328. Between theinput unit 328 and the output unit 326 is a processing pathway thatincludes a plurality of components 330, 332, 334, 336, 338, and 340. Inthat regard, the components 330, 332, 334, 336, 338, and 340 arerepresentative of a combination of one or more of a processor, a memoryunit, a filter, an amplifier, and/or other component suitable forprocessing the signals received from the imaging device and controllingthe output signal sent from the output unit 326. The specificcombination of component types utilized is dependent upon the particularfield-of-view control technique(s) to be implemented by the system 320.In that regard, it is understood that the imaging system and, inparticular, the controller 322 may include any number and type ofelectronic components and/or circuitry for performing the field-of-viewcontrol techniques of the present disclosure. Further, it is understoodthat the various components of the controller may be implemented inhardware, software, firmware, and/or combinations thereof. In thatregard, it is also understood that two or more of the various componentsof the controller may be combined into a single hardware or softwarecomponent in some instances. Likewise, it is understood that a singlecomponent of the controller may be split into two or more hardware orsoftware components in some instances. Further, it is understood thatthe components of the controller need not be positioned within a singlechassis, but instead may be positioned in separate housings and/or bepositioned remote from one another. In that regard, it is understoodthat components of the system may communicate through wired and/orwireless protocols, including communications requiring connection over anetwork.

Referring now to FIGS. 21a-21k , shown therein is a series of imagesobtained from an imaging device having a visual marker according to anembodiment of the present disclosure. In that regard, the series ofimages show the path of a marker 350 between the frames. In that regard,to make visual identification of the location of the marker 350 easier,FIGS. 21a-21k have been annotated to include a box 350 that isrepresentative of the position of the marker. Each of the framesillustrated in FIGS. 21a-21k are spaced apart by an equal amount of timeor number of frames. In the specific data set utilized to generate FIGS.21a-21k , each of the illustrated frames is separated by 1 second and 9frames such that, for example, the image of FIG. 21a corresponds toFrame 0 taken at time 0, the image of FIG. 21b corresponds to Frame 10taken at time 1.0 s, the image of FIG. 21c corresponds to Frame 20 takenat time 2.0 s, and so on. Taken together, the series of images show themotion of the visual marker across the field of view of the imagingdevice. In the illustrated data set, the marker 350 begins within thefield of view of the imaging device (as shown in FIG. 21a ) movesfurther to the right until it is partially out of the field of view (asshown in FIG. 21f , for example) and then moves back to the left andfully within the field of view again (as shown in FIG. 21k ). Theimaging dataset corresponding to the series of images shown in FIGS.21a-21k are utilized below in the context of a thresholding algorithmand a running average algorithm for identifying the marker 350.

Referring now to FIGS. 22a-22k , shown therein is a series of imagesobtained from another imaging device having a visual marker according toanother embodiment of the present disclosure. In that regard, the seriesof images show the path of a marker 360 between the frames. In thatregard, to make visual identification of the location of the marker 360easier, FIGS. 22a-22k have been annotated to include a box 360 that isrepresentative of the position of the marker. Each of the framesillustrated in FIGS. 22a-22k are spaced apart by an equal amount of timeor number of frames. In the specific data set utilized to generate FIGS.22a-22k , each of the illustrated frames is separated by 1 second and 9frames such that, for example, the image of FIG. 22a corresponds toFrame 0 taken at time 0, the image of FIG. 22b corresponds to Frame 10taken at time 1.0 s, the image of FIG. 22c corresponds to Frame 20 takenat time 2.0 s, and so on. Taken together, the series of images show themotion of the visual marker across the field of view of the imagingdevice. In the illustrated data set, the marker 360 begins within thefield of view of the imaging device on the right hand side of the image(as shown in FIG. 22a ) moves left towards the middle of the image (asshown in FIGS. 22e and 22f , for example) and then moves back to theright to the right hand side of the image again (as shown in FIG. 22k ).The imaging dataset corresponding to the series of images shown in FIGS.21a-21k are utilized below in the context of a thresholding algorithmand a running average algorithm for identifying the marker 360.

Referring now to FIG. 23, shown therein is a graph 370 illustrating thetracking of the markers 350 and 360 of FIGS. 21a-21k and FIGS. 22a-22kaccording to an embodiment of the present disclosure. More specifically,the graph 370 illustrates the tracking of the markers 350 and 360utilizing a thresholding algorithm of the present disclosure. In thatregard, the thresholding algorithm operates based on the assumption thatthe marker 350, 360 will have the highest amplitude reflection within apredefined region, such as a particular depth of field range. Asdiscussed above with respect to FIGS. 17 and 18, because the region offocus of an imaging is typically much greater than the region of focuswhere the marker will appear, in most instances there will be no tissuereflectors of interest in the near-field of the imaging device.Accordingly, the presence of the marker can be identified by identifyinga reflector within a region of interest (e.g., a particular depth orrange of depths) in the imaging data that meets a predefined threshold.The particular region of interest may be selected based on factors suchas the expected depth of marker relative to the transducer, the angleposition of the marker relative to the total field of view, and/orcombinations thereof. The actual value(s) of the threshold utilized canvary greatly between imaging devices and even different applications ofa single imaging device. For example, in some instances the thresholdvalue is selected based on one or more of: system gain (analog anddigital, including depth gain control (“DGC”)), transmit amplitude,marker proximity to transducer, marker structure, marker orientation,marker material, manufacturing tolerances of markers, transducerinsertion loss, sensitivity, and/or combinations thereof. Accordingly,in some instances the threshold value ranges between about −40.0 dB andabout −10.0 dB, but in other instances the threshold value is outside(greater than or less than) the values of this range. With respect tothe datasets of FIGS. 21a-21k and FIGS. 22a-22k that were utilized ingenerating the graph 370, a threshold value of −20.0 dB was utilized. Inthat regard, line 372 of graph 370 is representative of the dataset ofFIGS. 21a-21k , while line 374 is representative of the dataset of FIGS.22a -22 k.

In some embodiments, the thresholding algorithm utilizes the followingto determine marker location:

${{if}\mspace{14mu}{\sum\limits_{u = {u\; 1}}^{u = {u\; 2}}\left( {u,v} \right)}} \geq A_{t}$Y(n) = v else Y(n) = NaN end

where:

A(u,v) is the echo amplitude at sample number u from samples u1 to u2for A-Scan v

Y(n) is an array of length n whose elements are A-Scans that contain firmarker.

This algorithm is performed for each A-Scan such that a one-dimensionaldepth value is provided for each A-scan. In some instances, the depthoutput is provided as a single sample out of all samples within a singleA-Scan. Accordingly, Y(n) provides an array of all the A-scans within aframe in which the threshold value is exceeded. The final markerlocation for the B-scan associated with a collection of A-scans isdetermined based on Y(n). In that regard, in some embodiments the finalmarker location is selected as the average of the sample numbers ofY(n). In other embodiments, the final marker location is selected as themedian of the sample numbers of Y(n). In yet other embodiments, othercomputational techniques are utilized to select the final markerlocation based on the collection of A-scans determined based on Y(n).

Referring now to FIG. 24, shown therein is a graph 380 illustrating thetracking of the markers 350 and 360 of FIGS. 21a-21k and FIGS. 22a-22kaccording to another embodiment of the present disclosure. Morespecifically, the graph 380 illustrates the tracking of the markers 350and 360 utilizing a running average algorithm of the present disclosure.In some instances, the running average algorithm utilizes an n-lineaverage filter. In that regard, the running average is computed for oneor more A-scans and subsequent A-scans are compared against that runningaverage. Accordingly, if the peak reflector in an A-scan exceeds thepeak of the running average by a predefined threshold, then the markerhas been detected. Similar to the thresholding algorithm discussedabove, in some instances only a region of interest (e.g., a particulardepth or range of depths) of the image data is considered whenevaluating in the imaging data to identify the marker. In that regard,the particular region of interest may be selected based on factors suchas the expected depth of marker relative to the transducer, the angleposition of the marker relative to the total field of view, and/orcombinations thereof.

The presence of the marker can be identified by identifying a reflectorwithin the region of interest that is greater than the running averageby a predefined threshold. Again, the actual value(s) of the thresholdutilized can vary greatly between imaging devices and even differentapplications of a single imaging device. For example, in some instancesthe threshold value is selected based on one or more of: system gain(analog and digital, including depth gain control (“DGC”)), transmitamplitude, marker proximity to transducer, marker structure, markerorientation, marker material, manufacturing tolerances of markers,transducer insertion, sensitivity, and/or combinations thereof.Accordingly, in some instances the threshold value ranges between about5 dB and about 10 dB, but in other instances the threshold value isoutside (greater than or less than) the values of this range. Withrespect to the datasets of FIGS. 21a-21k and FIGS. 22a-22k that wereutilized in generating the graph 380, a threshold value of 8.5 dB wasutilized. In that regard, line 382 of graph 380 is representative of thedataset of FIGS. 21a-21k , while line 384 is representative of thedataset of FIGS. 22a-22k . The reflector in this data also requires athreshold, which is determined to be 8.5 dB in this data set. Thisreflector is also highly variable, again dependent on the applicationsettings.

In contrast to the thresholding algorithm discussed above, the runningaverage algorithm operates on an entire B-Scan as opposed to singularA-scans. In that regard, in some instances the running average algorithmutilizes the following to determine marker location:

${A_{avg}\left( {u,v} \right)} = \frac{\sum\limits_{v}^{v + {\Delta\; v}}{A\left( {u,v} \right)}}{\Delta\; v}$${{if}\mspace{14mu}\frac{\sum\limits_{u = {u\; 1}}^{u\; 2}{A\left( {u,v} \right)}}{{u\; 2} - {u\; 1}}} \geq \frac{\sum\limits_{u = {u\; 1}}^{u\; 2}{A_{avg}\left( {u,v} \right)}}{{u\; 2} - {u\; 1}}$Y(n) = v else Y(n) = NaN endwhere:

-   A_(avg) is the average echo amplitude over Δv A-scans for a sample u

$\frac{\sum\limits_{u = {u\; 1}}^{u = {u\; 2}}{A\left( {u,v} \right)}}{{u\; 2} - {u\; 1}}$

-    is the mean echo amplitude for an A-Scan v for samples between u2    and u1

$\frac{\sum\limits_{u = {u\; 1}}^{u = {u\; 2}}{A_{av}{g\left( {u,v} \right)}}}{{u\; 2} - {u\; 1}}$

-    is the mean echo amplitude for group of Δv A-Scans for samples    between u2 and u1 Y(n) is an array representing all A-Scans that    contain the marker

As a result, Y(n) provides an array of all the A-Scans in which themarker is detected within a frame. The final marker location for theB-scan is determined based on Y(n). In that regard, in some embodimentsthe final marker location is selected as the average of the samplenumbers of Y(n). In other embodiments, the final marker location isselected as the median of the sample numbers of Y(n).

Referring now to FIGS. 25-27 b, shown therein are aspects of a method400 of controlling a control signal of an imaging system according to anembodiment of the present disclosure incorporating a correlation ortemplate matching algorithm. Referring more specifically to FIG. 25,shown therein is a flow chart of the method 400. The method 400 startsat step 402 where a template of the marker is obtained. In that regard,imaging markers typically have unique or at least identifiablecharacteristics that is consistent from frame to frame, which may beconsidered the marker's signature. Accordingly, these identifiablecharacteristics of the marker can be utilized to create a templatesuitable for identifying the marker in other imaging data or frames. Dueto various reasons, different imaging devices will have variances in thephysical shape and location of the marker(s). As a result, a marker ofone device can have different characteristics than a marker of anotherdevice (including devices intended to be identical to one another). As aresult, in some instances a unique or custom template is utilized foreach imaging device. In other instances, a common template is utilizedacross a plurality of imaging devices having similar arrangements and/ormarkers.

A template for a particular imaging device or group of imaging devicescan be computed in a variety of ways. In some instances, the template isdetermined during manufacturing and stored on a memory device (such asan RFID tag) associated with the device. In other instances, thetemplate is calculated at the beginning of use (in some instances, eachuse) by initially over-driving the actuator to ensure that the markerwill be present in the initial image(s) obtained by the device. Thelocation of the marker within the initial images is determined usingthresholding methods (e.g., the thresholding algorithm discussed above),averaging methods (e.g., the running average algorithm discussed above),and/or other suitable techniques. In some instances, the template is apixel intensity map or heat map of a region of an image containing themarker. For example, FIG. 26 provides an image 420 showingidentification of a region 422 associated with a marker suitable forcreating a template for use in a correlation algorithm of the presentdisclosure. More specifically, FIG. 26 is a pre-scan converted imagewhere the x-axis is A-scan number (corresponding to angle), the y-axisis depth, and the color intensity indicates returned signal in dB. Byidentifying the marker within the image, a region 422 containing themarker can be utilized as the template.

Referring again to FIG. 25, once this template is obtained at step 402,the method 400 continues at step 404 by computing the correlation of thetemplate with the current image. In other words, the method determineshow closely the current image matches the template. In some instances,the following equation is utilized to calculate the cross correlation ofthe template and the image at position (u,v):

${C\left( {u,v} \right)} = \frac{\sum_{x,y}{\left\lbrack {{I\left( {x,y} \right)} - {\overset{\_}{I}}_{u,v}} \right\rbrack\left\lbrack {{T\left( {{x - u},{y - v}} \right)} - \overset{\_}{T}} \right\rbrack}}{\sqrt{\sum_{x,y}{\left\lbrack {{I\left( {x,y} \right)} - {\overset{\_}{I}}_{u,v}} \right\rbrack^{2\;}{\sum_{x,y}\left\lbrack {{T\left( {{x - u},{y - v}} \right)} - \overset{\_}{T}} \right\rbrack^{2}}}}}$where:

-   C(u,v) is the normalized cross-correlation with the template    centered at pixel (u,v) where u indicates the depth location and v    indicates the A-scan number-   I is the original image-   T is the template-   Ī_(u,v) is the mean of the original image in the region overlapping    the template-   T is the mean of the template

This equation is applied to all pixels within the search region orregion of interest of the image such that the cross correlation of thetemplate and the current image is calculated for various possiblelocations of the marker. The correlation is maximized where the templateand image have the strongest match in relative amplitude. Since themarker is typically at a fixed location relative to the transducer, themarker will appear at approximately the same y distance (i.e., depth ordistance along an A-scan) in each image.

Referring more specifically to FIGS. 27a and 27b , shown therein is aplot of the cross-correlation results for an exemplary frame of adataset relative to a corresponding template. In that regard, FIG. 27ais a heat map 430 showing the cross-correlation values for the entireimage, including an area 432 of maximum correlation. FIG. 27b is aclose-up of the portion of the heat map of FIG. 27a containing the area432 of maximum correlation. While FIG. 27a illustrates thecross-correlation values for the entire image, in some instances it isnot necessary to calculate the cross-correlation for the entire imagesince the marker will only be detected in a limited region of interestof the image. Accordingly, to increase computational speed thecorrelation is computed for the pre-defined search region in someinstances. In that regard, the selected search region may be selectedbased on one or more of the following: distance of marker relative tothe transducer (i.e. depth marker should appear in image), angularextent of marker relative to the FOV, maximum anticipated frame-to-framechange in angle coverage, marker location in previous image, and/orcombinations thereof.

Referring again to FIG. 25, in some instances, the method 400 continuesat step 406 by limiting the search region of the image based on one ormore of the factors discussed above. In some particular embodiments, thesearch region is limited based on image depth and the maximum expectedtransducer motion from one frame to the next. In other embodiments, themethod 400 omits step 406 and the entire image is utilized as the searchregion.

At step 408, the point of maximum correlation between the image and thetemplate is identified within the limited search region (if the method400 includes step 406) or within the entire image (if step 406 isomitted). In that regard, the point where the correlation is maximizedis considered to be the marker location. Once the marker location isfound for the current frame at step 408, the method 400 continues atstep 410 where the marker position is updated and the energy provided tothe actuator of the imaging device that imparts motion on the imagingelement is adjusted, as necessary, to ensure that the motion profile ofthe imaging transducer results in the marker being at the desiredlocation(s) within the subsequent image(s). Steps 404, 406 (optional),408, and 410 are repeated continuously or intermittently duringoperation of the imaging device to control the field of view of theimaging device.

Referring now to FIGS. 28-31, shown therein are aspects of a method 440of controlling a control signal of an imaging system according to anembodiment of the present disclosure incorporating a reverse correlationor template matching algorithm. In that regard, the reverse correlationalgorithm uses a template matching technique similar to that discussedabove with respect to method 400, but the template is formed based on aregion of image data that does not contain the marker. For example, insome embodiments, the first portion of the image depth of an imagingdevice (e.g., less than 10 mm, less than 5 mm, or otherwise) has areturn signal with fairly constant amplitude across A-scans except whenthe marker is present. Accordingly, by generating a template of theconstant amplitude signal (i.e., non-marker region) and correlating thatwith a frame, the correlation will be high everywhere except where themarker is located. Since this version of the template matching algorithmis looking for areas with low correlation to the template to identifythe marker region, it is referred to as a reverse correlation algorithmin some instances.

Referring more specifically to FIG. 28, shown therein is a flow chart ofthe method 440. The method 440 starts at step 442 where a template ofthe non-marker or constant signal region is obtained. In that regard,FIG. 29 provides an image 460 showing identification of a constantsignal region 462 suitable for creating a template for use in acorrelation algorithm of the present disclosure and a region 464associated with the presence of a marker. More specifically, FIG. 29 isa pre-scan converted image where the x-axis is A-scan number(corresponding to angle), the y-axis is depth, and the color intensityindicates returned signal in dB. By identifying the region 462 withinthe image where the marker could be found but is not found, a templatecan be created. In that regard, in some instances the template is formedby one or more unfiltered samples within the region 462. In otherinstances, the template is defined by filtering across multiple A-scans.For example, the template is defined by averaging across a plurality ofA-scans within the region 462 in some embodiments.

Referring again to FIG. 28, once the template is obtained at step 442,the method 440 continues at step 444 by computing the correlation of thetemplate with the current image. In other words, the method determineshow closely the current image matches the template. In some instances,the same equation discussed above with respect to the cross correlationcalculation of the method 400 is utilized to calculate the crosscorrelation of the template and the image at position (u,v) for method440. In that regard, the equation is applied to all pixels within thesearch region or region of interest of the image such that the crosscorrelation of the template and the current image is calculated forvarious possible locations of the marker. The correlation is maximizedwhere the template and image have the strongest match in relativeamplitude and minimized where the template and image have the weakestmatch.

Referring more specifically to FIG. 30a , shown therein is a plot of thecross-correlation results for an exemplary frame of a dataset relativeto a corresponding template. In that regard, FIG. 30a is a heat map 470showing the cross-correlation values for an entire image, but where amarker is expected to be visible at a depth of approximately 36 pixels.Referring again to FIG. 28, in some instances, the method 440 continuesat step 446 by limiting the search region of the image based on one ormore of the factors discussed above with respect to the method 400. Insome particular embodiments, the search region is limited based on imagedepth. For example, FIG. 30b provides a graph 472 showing thecross-correlation values at the 36^(th) pixel depth.

Referring again to FIG. 28, at step 448, the point of minimumcorrelation between the image and the template is identified within thelimited search region (if the method 400 includes step 446) or withinthe entire image (if step 446 is omitted). In that regard, the pointwhere the correlation is minimized is considered to be the markerlocation. For example, where the search region is limited to a pixeldepth of 36, as shown in FIG. 30b , the point of lowest correlation isidentified near A-scan 185. FIG. 31 is a heat map 480 showingcross-correlation between a template and a dataset of 200 frames. Therelative position of the marker between frames is illustrated by thecross-correlation values. In some instances, the marker location isdetermined by the median A-scan meeting a threshold of minimumcorrelation (i.e., A-scans having a cross-correlation less than thethreshold value).

Referring again to FIG. 28, once the marker location is found for thecurrent frame at step 448, the method 440 continues at step 450 wherethe marker position is updated and the energy provided to the actuatorof the imaging device that imparts motion on the imaging element isadjusted, as necessary, to ensure that the motion profile of the imagingtransducer results in the marker being at the desired location(s) withinthe subsequent image(s). Steps 444, 446 (optional), 448, and 450 arerepeated continuously or intermittently during operation of the imagingdevice to control the field of view of the imaging device.

Referring now to FIGS. 32-46, shown therein are aspects of techniquesfor controlling a control signal of an imaging system according toembodiments of the present disclosure that incorporate asum-of-differences and sum-of-gradient algorithm. In some embodiments,the sum-of-differences and sum-of-gradient algorithm utilizes at leastsome features similar to the methods of identifying a marker discussedabove. Referring to FIG. 32, shown therein is a flow chart of a method500 incorporating a sum-of-differences and sum-of-gradient algorithm.The method 500 begins at step 502 where the gradient is computed over apre-defined region across A-scans of an image (B-scan). In that regard,the pre-defined region or region of interest of the image may beselected based on one or more of the factors discussed above withrespect to the other techniques for identifying the marker. The computedgradient within the pre-defined region are summed for all pixels withinthat region. In some instances the gradient of the image is approximatedusing a convolution operator. For example, in some embodiments thefollowing convolution operator is utilized:SOG=Σ _(u,v) G _(x)(u,v)G _(x) =I _(L)*[−1 0 1]

-   -   where:    -   is the final Sum of Gradients for a single frame    -   G_(x) is an approximation of the horizontal gradient    -   I_(L) is the original image limited to the SOG region    -   * indicates the 2D convolution operator

At step 504 of the method 500, the sum-of-gradient calculation isutilized to determine if there is sufficient transducer motion towarrant attempting to identify a marker location. In that regard, thesum-of-gradient is compared to a threshold value indicative oftransducer motion. If there is very little motion, then the image willhave fairly constant signal across A-scans and, therefore, acorresponding low gradient. In that regard, FIG. 33 shows an image 520that is representative of limited or no transducer motion. FIG. 33 alsoincludes a graphical representation 522 that shows the gradient forregion 524 of image 520. As shown in the graphical representation 522,the region 524 has a low and relatively constant gradient. In contrast,FIG. 34 shows an image 530 that is representative of full-rangetransducer motion. FIG. 34 also includes a graphical representation 532that shows the gradient for region 534 of image 530. As shown in thegraphical representation 532, the region 524 has a widely varyinggradient that is indicative of transducer motion. In some instances, todetermine if sufficient motion is present for the marker to be presentin the image, the absolute value of the gradient is summed across allpixels in the region of interest and compared to the threshold value.

For example, FIG. 35 provides a line graph 540 of the sum-of-gradientacross a plurality of frames of a dataset. As shown, the graph 540includes a line 542 that is representative of the value of thesum-of-gradient for each frame of the dataset and a line 544 that isthreshold value for the dataset. As shown, the first approximately 75frames have insufficient gradient to meet the threshold valuerepresented by line 544, while the remaining frames exceed the thresholdvalue. In some instances the failure to meet the threshold value is anindication that more energy is needed to move the transducer to reach adesired field-of-view. Accordingly, in some embodiments, when thesum-of-gradient does not meet the threshold value the method 500continues by increasing the power or energy provided to the actuator ofthe imaging device and returns to step 502 to calculate the resultinggradient. This process of increasing the power or energy and calculatingthe gradient is repeated until the sum-of-gradient meets the thresholdvalue, exceeds the threshold value by a predetermined amount, meets thethreshold value for a predetermined number of frames, and/or otherwisereaches a gradient value that indicates the presence of the marker islikely to be found in the image(s).

Referring again to FIG. 32, once the sum-of-gradient passes thethreshold value for a frame, the method 500 continues to step 506 wherethe pixel values are clamped to the mean value across all A-scans.Clamping is done in step 506 to prevent extremely low intensity pixelsaround the marker from interfering with detection of the marker using asum-of-differences calculation. In that regard, FIG. 36 provides a firstgraphical representation 550 of a region containing a marker beforeclamping or without clamping and a second graphical representation 552that shows the same region after clamping. As shown, clamping cleans upthe image by removing the low-intensity pixels from around the marker.In some instances, the following calculations are utilized to performthe image clamping:

if  I(u, v) < M_(row)(u) I_(clamp)(u, v) = M_(row)(u) elseI_(clamp)(u, v) = I(u, v) end${M_{row}(u)} = \frac{\sum\limits_{i = 1}^{n_{A}}{I\left( {u,v} \right)}}{n_{A}}$

-   -   where:    -   I_(clamp) is the resulting image after clamping the minimum        value to the the mean of each row    -   M_(row)(u) is the mean of the image across row (or depth        position) u    -   I(u,v_(i)) is the value of the image at row (or depth position)        u and column (or A-scan) v    -   n_(A) is the total number of columns (A-scans) in the image        In some instances, the method 500 omits step 506.

Referring again to FIG. 32, after clamping of the image at step 506, themethod 500 continues to step 508 where the sum-of-differences iscalculated. In that regard, for identification of the marker, thesum-of-differences algorithm assumes that the A-scans that contain themarker will have a higher amplitude within the pre-defined region ofinterest (e.g., depth(s) where marker is expected to be detected) thanthe other A-scans of an image. In order to detect this difference inamplitude, a sliding window is compared with a window at half the A-scandistance (i.e., half the angle) and the difference between the twowindows are summed. For example, in some instances the followingequation is utilized:

${{SOD}\left( {u,v} \right)} = {\sum\limits_{x = {u - h}}^{u + h}{\sum\limits_{y = {v - w}}^{v + w}\left\lbrack {{I_{clamp}\left( {x,y} \right)} - {I_{clamp}\left( {x,{y - {{ceil}\left( \frac{v}{2} \right)}}} \right)}} \right\rbrack}}$

-   -   where:    -   SOD(u,v) is the final Sum of Difference for position(u,v)    -   I_(clamp) is the clamped image    -   h is half the height of the desired SOD window    -   w is half the width of the desired SOD window    -   ceil is the ceiling operator

Referring to FIG. 37, shown therein is an image 560 illustrating the useof windows as discussed above. In that regard, image 560 includes afirst window 562 and a second window 564 that are used for differencing.In particular, the sum-of-differences is calculated as the sum of thedifference between the window 564 and the window 562. In that regard,window 564 is translated from left to right across the image. As window564 is moved across the image 560, window 562 is also moved across theimage such that window 562 is positioned halfway between A-scan #1 andwindow 564. FIG. 38 provides a heat map 570 showing detection of animaging marker by computing the sum-of-differences across all A-scansand frames of a sample dataset. The marker is visually identified in theheat map 570 as the region with the highest sum-of-differences. In thatregard, a final marker position is also identified for each image. Inthat regard, the specific A-scan(s) associated with the final markerposition of an image may be selected based on the mean value, the medianA-scan of A-scans where the marker is present, and/or any other suitablemethod. This sum-of-differences technique exploits the fact that, forimaging devices where the marker is positioned at or near the end of thedesired range of motion of the transducer, when the transducer is athalf its full range of motion the region of interest should have arelatively low amplitude signal and when the transducer is at itsmaximum field of view the region of interest should have the highestaverage amplitude due to the presence of the marker.

Referring again to FIG. 32, once the sum-of-differences is computed foran individual frame, the method 500 continues at step 510 where thesearch region or region of interest is then limited based on theprevious marker location and the maximum movement between frames. Insome embodiments, the method 500 omits step 510. After step 510, themethod 500 continues to step 512 where the sum-of-differences iscompared to a threshold value. In that regard, the threshold may bebased on one or more of the following: an average sum-of-differences, amaximum sum-of-differences, a minimum sum-of-differences, a mediansum-of-differences, an absolute threshold based on the expected minimumvalue, and/or combinations thereof. In some instances, multiplethreshold values are utilized. For example, in one embodiment twothreshold values are utilized. The first threshold is utilized to findall points above a minimum average sum-of-differences, while the secondthreshold is utilized to find all points that have a sum-of-differencesvalue of greater than a certain percentage of the maximumsum-of-differences. Accordingly, A-scans that are above both of thesethresholds are indicative of marker detections. In some particularembodiments of the present disclosure, the minimum averagesum-of-differences is between about 1 dB and about 15 dB, but in otherinstances the minimum average sum-of-differences is outside (greaterthan or less than) the values of this range. In some instances, thepercentage of the maximum sum-of-differences is between about 25% andabout 90%. In one specific implementation of such a technique, theminimum average sum-of-differences is 3 dB and the percentage of themaximum sum-of-differences is 75%. As noted above, the final markerposition can be defined as the median A-scan of A-scans where the markeris present, a mean of the A-scans where the marker is present, and/orany other suitable method. In some embodiments of the method 500, thesum-of-differences is utilized without the sum-of-gradient thresholdcalculations. In other words, in some embodiments, steps 502, 504, and506 are omitted.

Referring now to FIG. 39, shown therein is a flow chart for a method 600of implementing a sum-of-gradient and sum-of-differences algorithmaccording to an embodiment of the present disclosure. The method 600begins at step 602 with acquiring image frame data. Once the frame datahas been acquired, then the method 600 continues at step 604 where thedata is loaded into a sum-of-gradient algorithm. The loaded data islimited to a predefined depth region in which significant signalvariation is expected when the transducer is moving. With the dataloaded and limited to the region of interest, the method 600 continuesat step 606 where the sum-of-gradient is calculated and then compared toa threshold at step 608.

If the sum-of-gradient for the current frame is below the threshold,then the method 600 continues to step 610 where it is determined howmany times this condition has been reached (hysteresis). Morespecifically, the number of times a frame has been below thesum-of-gradient threshold is compared to a threshold value M. If morethan M frames have been below the sum-of-gradient threshold, then themethod 600 continues to step 612 where the current or power supplied tothe actuator of the imaging device is increased. The counter for thenumber of frames failing to satisfy the sum-of-gradient threshold isthen increased at step 614. If less than M frames have been below thesum-of-gradient threshold, then the method 600 skips step 612 and simplyincreases the counter at step 614. After increasing the counter, themethod 600 returns to step 602 and acquires the next frame data.

If the sum-of-gradient for the current frame is above the threshold atstep 608, then the method 600 continues to step 616 where the data isloaded into a sum-of-differences algorithm. At step 618, thesum-of-differences is calculated. At step 620, the maximum of thesum-of-differences is identified. At step 622 the maximumsum-of-differences is compared to a threshold. If the maximumsum-of-differences is below the sum-of-differences threshold, then themethod 600 continues to step 624 where it is determined how many frameshave failed to satisfy the sum-of-differences threshold. Morespecifically, the number of times a frame has been below thesum-of-differences threshold is compared to a threshold value M. If morethan M frames have been below the sum-of-differences threshold, then themethod 600 continues to step 626 where the current or power supplied tothe actuator of the imaging device is increased. The counter for thenumber of frames failing to satisfy the sum-of-differences threshold isthen increased at step 628. If less than M frames have been below thesum-of-differences threshold, then the method 600 skips step 626 andsimply increases the counter at step 628. After increasing the counterat step 628, the method 600 returns to step 602 and acquires the nextframe data.

If the maximum sum-of-differences is above the sum-of-differencesthreshold, then the method 600 moves on to step 630 where the markerlocation is determined. In that regard, the marker location isdetermined based on calculation of a mean, median, and/or combinationsthereof in some instances. After determining the marker location, themethod 600 continues to step 632 where it is determined if the markerlocation is within an acceptable margin of error (e.g., distance, angle,etc.) around a desired marker position. In the illustrated embodiment,it is presumed that the desired position for detection of the marker isa position that corresponds to a rotation of 120 degrees relative to astarting point of the transducer. Generally, the desired position andthe region of error around that position may be considered the targetregion of the image for the marker. If the marker is within the targetregion, then the current or power supplied to the actuator of theimaging device is maintained at its current level and the counter is setto 0 in step 634. If the marker is not within the target region, thenthe method 600 continues to step 636 where it is determined how manyframes have failed to have the marker within the target region. Morespecifically, the number of times a frame has had the marker outside ofthe target region is compared to a threshold value M. If more than Mframes have had the marker outside of the target region, then the methodcontinues to step 638 where the current or power supplied to theactuator of the imaging device is decreased. After decreasing thecurrent or power, the counter for the number of frames failing tosatisfy the sum-of-differences threshold is then increased at step 640.If less than M frames have had the marker outside of the target region,then the method 600 skips step 638 and simply increases the counter atstep 640. After increasing the counter at step 640, the method 600returns to step 602 and acquires the next frame data.

While the sum-of-differences and sum-of-gradient algorithms includecomplex calculations and analysis, embodiments of the presentapplication provide hardware implementations that can determine themarker position utilizing the sum-of-differences and sum-of-gradientalgorithms within the same amount of time it takes to perform twoB-scans with the scanning mechanism. Referring now to FIG. 40, one suchhardware implementation 650 is illustrated. The illustrated hardwarearchitecture is suitable for use with the image processing systems wherethe “corner-turn” (transpose) operation is performed in hardware, but itshould be apparent to a skilled practitioner that the same approach maybe readily used for other image acquisition and processing architecturesincluding those that do not utilize a corner turn.

In general, the calculations of the sum-of-gradient andsum-of-differences algorithms (e.g., thresholds, sum-of-gradient,sum-of-differences, etc.) operate on decibel power data for theultrasound return. While the processing operations typically used toproduce images for the clinical operator/end-user involve complexlateral filtering, autocorrelation for color Doppler flow, and othercomplex processing techniques, such steps are not required for effectivefield-of-view control. Accordingly, in some implementations of thesum-of-gradient and sum-of-differences algorithms it can be moreimportant that the threshold average and the sum-of-differencesoperations use the same definition of signal power than that the mostaccurate signal power information is found for each point.

As shown in FIG. 40, the hardware architecture 650 includes a domain 652configured to handle the A-scan data and a domain 654 configured tohandle the B-scan data. The hardware architecture receives ultrasound orother imaging signals and performs pre-memory processing at 656. Asimplified signal power calculation is utilized. In the illustratedembodiment, the power is calculated as log₂|x_(i)| at both 658 and 672,where x_(i) denotes each pair of I/Q values that comprise thepre-memory-processed ultrasound signal or equivalent of other imagingsignals (e.g., the complex Fourier-transform output values of afrequency-domain OCT or other electromagnetic depth imaging signal).Wherever a dBFS (Decibels Relative to Full Scale) value is used in thealgorithm (e.g. 2 dBFS), the equivalent digital value would beapproximately one-sixth that value (e.g., 2 dBFS≈0.33). Accordingly, iflog₂(I_(i) ²+Q_(i) ²) is used to approximate dBFS while eliminating thesquare-root operation implied by log₂|x_(i)|, then translating betweendBFS and digital values includes both gain and offset terms.

The sum-of-gradient calculation involves a filtering convolution alongthe B-scan axis that can be performed efficiently on the post-transposeddata. However, the sum-of-gradient calculation can also be performed onthe A-scan data, as shown at 666. In some instances, the sum-of-gradientcalculation is performed with the use of a two-A-scan buffer. If thethreshold averaging and the sum-of-gradient operations are bothimplemented in the A-scan domain 652, then the results can be ready intime for the start of the corresponding sum-of-differences calculations664 on the same B-scan frame. This approach minimizes latency in thecontrol loop to less than one frame-time (e.g., 50 ms at 20 Hz) from theend of a B-scan to the time the corresponding sum-of-differences resultis available to the field-of-view controller. As shown in FIG. 40, thethreshold averages are computed for each A-scan at 660 and then writtento a FIFO buffer 662 where they can be retrieved by thesum-of-differences module 664. The sum-of-differences calculation canalso leverage the corner-turn 668 and external memory 670 and beperformed on an entire frame or set of A-scans. In that regard, afterpassing through the corner turn 668, at least a portion of the imagingdata is sent through post-memory processing at 674.

If the sum-of-gradient test for a given frame has a negative outcome,then the sum-of-differences calculation can either be bypassed or itsoutput can be dumped. However, when the sum-of-gradient test issuccessful, then the sum-of-differences calculation is utilized todetermine the optimal field-of-view. The threshold averaging may beperformed regardless of whether the sum-of-gradient test is met.Accordingly, the threshold averaging is implemented in parallel hardwarefor best performance in some instances. In that regard, FIG. 41 providesa timeline 680 that illustrates the timing of the operations.

As described, to determine whether a given frame contains an adequaterange of motion to find the marker, the sum of horizontal (B-scan-axis)gradient magnitudes over a representative region is calculated. Forexample, for one exemplary data set, the region of interest wasconsidered to be all samples of rows 150-200 and the average gradientthreshold was set to 2 dBFS. In some instances, the gradient filtercoefficients are [−1 0 +1] and can be realized by the hardwarearrangement shown in FIG. 42. In that regard, as each new A-scan datapoint arrives at 692, the value from two A-scans previous is justemerging from the 2-A-scan buffer 694 and is subtracted from the newincoming value at 696. This effectively transposes the data into theB-scan domain in a very limited fashion. The relevant subset of thesegradient values are summed at 698 and compared against the threshold 702at 700. As noted above, the threshold value for implementation of onedataset was set to 2 dBFS per point in the sum. In some instances, thekernel is non-normalized with an effective gain of 2, and because thevalues are being summed rather than averaged, the threshold becomes 4dBFS times the number of samples in the region of interest. If thesum-of-gradient is below this threshold, then the sum-of-differencescalculation should be ignored and the field of view should be expandedso that the marker becomes visible (i.e., the drive current to thetransducer deflection mechanism should be increased).

As described in the clamping section of the sum-of-differences algorithmcalculations above, the left half of the sum-of-differences region(e.g., the first 180 A-scans of a 360-A-scan image) is summed along theB-scan axis to produce a threshold value for each depth point in thesum-of-differences region (e.g., 45 samples along the A-scan axis asrequired to cover the intra-window image depth region). This summationcan be performed on-the-fly as A-scan data arrives from the processingmodules, as shown in the arrangement 710 of FIG. 43. In that regard, adual-port RAM 712 allows the accumulators for each depth point to beinitialized during the first A-scan and updated with each subsequentA-scan until the entire left half of the sum-of-differences region hasarrived. As the last A-scan of the left half of the sum-of-differencesregion arrives, each sum is normalized to form an average. These averagevalues for each of the depth points are then transferred to thesum-of-differences module. A ping-pong double-buffer based on a separatedual-port RAM provides a convenient mechanism for this transfer, asshown in FIG. 40 above. While the sum-of-differences module is using thethreshold data from the previous frame, the threshold averaging modulemay be updating the dataset corresponding to the frame that is currentlyarriving. In effect, this double buffer is synchronized to thecorner-turn buffer, but because it is small and data elements must beread many times it is more efficient to utilize resources within theFPGA or ASIC.

The averaging region can be constrained to a subset that forms apower-of-two number of elements so that no division is required. Forexample, if the left half of the sum-of-differences region nominallyuses 180 of the 360 A-scans in a frame, then only N=128 of those 180A-scans are averaged in some instances. Alternatively the sum of theregion can be multiplied by the reciprocal of the number of accumulatedpoints (with the reciprocal expressed in fixed point representation orfloating point representation in some embodiments).

Once the sum-of-gradient and threshold average (clamping) values havebeen computed and transferred to the B-scan domain as discussed abovewith respect to FIG. 40, the sum-of-differences calculation isperformed. In that regard, FIG. 44 illustrates a module 750 suitable formaking the sum-of-differences calculation. As discussed above, thecentral calculation described above from which the algorithm takes itsname is a sum-of-differences between two blocks. The size of the blocksvaries based on the imaging device, size of the region of interest, sizeof the images, and/or other factors. In one embodiment, each block has asize of approximately 20 pixels by 45 pixels, covering a total of 900pixels. In that regard, the width of each block (e.g., 20 pixels) is aparameter of the sum-of-differences module that is determined by thenumber of A-scans that are required to span a certain percentage orangle (e.g. 10% or a 10° angle) out of the desired field of view (e.g.120° field of view). The module 750 is configured to iterate over thepossible block locations or block numbers.

The block address generation module 752 takes a block number as inputand generates the addresses of the left and right blocks to bedifferenced. These addresses are fed to a memory interface and arbiter754 that shares the external memory 758 bandwidth between thecorner-turn 758 A-scan write, corner-turn 758 B-scan read, andsum-of-differences block read operations.

The Y addresses (A-scan axis) of each block are fixed. The X addresses(B-scan axis) are given as: Right block X addresses: (location−width+1)to (location+width); and Left block X addresses:[round(location/2)−width+1] to [round(location/2)+width]. Note that the“width” referred to above is half the nominal block width (e.g., 10versus 20). Rounding division by 2 can be accomplished by adding 1 andthen shifting right by 1. Each (X,Y) coordinate translates into a memoryoffset as follows: Address=(X*A-scan length)+Y.

The block address generation module also outputs the address required toread a threshold from the threshold double-buffer 760 that correspondsto the current depth point along the A-scan axis. As discussed above,this dual-port double-buffer is written by the threshold averagingmodule. Having a true second port allows the sum-of-differences moduleto read thresholds at full speed from the “old” half of the buffer evenwhile the threshold averaging module may be writing to the “new” half ofthe buffer.

Corresponding sample points in the first and second blocks have the samedepth (along the A-scan axis) by definition, so the threshold is alwaysthe same for each pair of samples read from the external memory 756.Therefore, the same threshold value is fed to two parallel blocks 762,764 that clamp each of the two corresponding samples to the minimumvalue for their particular depth at 766. The output of thesum-of-differences calculation 768 feeds an internal memory 770 and arunning maximum calculation 772. The maximum 772 is one of two inputs toa thresholding operation that identifies those blocks satisfying thethreshold(s). For example, in one embodiment the thresholding operationidentifies the blocks whose sum-of-differences is both: ≧75% of themaximum sum-of-differences for this frame and ≧3 dBFS on average. Theresult of this testing is a stream of logical values, or one bit foreach block, indicating whether the block met the threshold(s). In theillustrated embodiment, the marker location is selected as the median ofall blocks that meet the thresholds.

In some embodiments, the median is determined by storing the bits in aFIFO while also counting the number of “1” bits encountered, as shown inthe arrangement 780 of FIG. 45. Once all of the bits are stored in theFIFO, the count is used to find the median block. There are threepossible scenarios: (1) No blocks met the threshold tests: marker notfound (if this condition persists for some period of time, then increasethe field of view); (2) An odd number of blocks were found (then thesame location will be found by both of the paths, and averaging willhave no effect); or (3) An even number of blocks were found (then thetwo center-most locations will be averaged).

Referring now to FIG. 46, the hardware architecture also relies on acontrol loop 790. A process variable (PV) is determined at 792. In someinstances, the process variable is the position of the marker determinedabove as an integral number of A-scans (or an integral number+0.5 if themedian spans two peak points). In some instances, rounding mechanisms(such as floor, ceiling, etc.) are utilized to select the position ofthe marker. Based on the values of the process variable and thesetpoint, a manipulated variable is adjusted at 794. In that regard, insome instances the setpoint is an integral number of A-scanscorresponding to the field of view location (e.g., 120 degrees). Theobjective of the controller is to adjust the manipulated variable toachieve a desired result, such as transducer deflection (actuator) thatachieves the optimal or desired field of view. In some instances, to dothis as quickly as possible, the relationship between the actuator driveinput and field of view location output is characterized and its inverseapproximated at 796. This information allows the control system tooperate in a linear space that, in turn, maximizes its stable bandwidth.Based on the adjustments to the manipulated variable at 794 and theinverse transform at 796, the transducer is deflected at 798. A newprocess variable is determined at 792 based on the updated transducerdeflection and the process repeats to facilitate control of the field ofview of the imaging device.

In some instances, the transducer deflection does not have awell-defined relationship or transfer function between the manipulatedvariable (MV) (e.g. current or power) and the effect on the processvariable (i.e., field of view). For example, the transfer function maynot be readily inverted (e.g. due to hysteresis or other memoryeffects), the transfer function may vary from one interchangeable sensorunit to another such unit; the transfer function may vary over time as agiven sensor unit is used; and the transfer function may have bothrandom and deterministic jitter components (fast variations in time).

One way to compensate for these challenges is to make the smallestpossible incremental changes in the manipulated variable (MV) in thedirection needed to move the PV to the setpoint. This avoids the needfor an inverse transformation or approximation, but may put moreconstraints on the ability of the system to compensate for variations onshorter time scales that may be deterministic (either periodic orserially correlated). Applying PID design techniques to determine thePID parameters results in faster times to achieve the optimal field ofview in some instances. Further, modeling and/or locking todeterministic jitter patterns further improves this performance byadding a feed-forward component to the controller.

A cascaded controller may also be used if a secondary parameter can besensed at a higher rate than the primary parameter (i.e., markerlocation). For example, if the current flow or total energy delivered tothe actuator is predictive of its deflection but the resistance of theactuator varies, causing jitter, then the marker control loop may beused to drive the setpoint of the actuator driver control loop which inturn servos another parameter such as drive voltage, pulse shape, slewrate, or otherwise to achieve the requested current or energy delivery.If the random (i.e. uncompensated) jitter magnitude is known, then PVchanges less than this magnitude may need to be suppressed byhysteresis. Another solution that results in a more optimal PIDcontroller is to use gain scheduling to reduce the bandwidth of thecontroller as the error decreases, but keeping the integral gainparameter K_(i)>0 to ensure convergence.

In some implementations a Viterbi algorithm is utilized to determine thelocation of the marker in one or more of the field of view controltechniques described above. For example, in some embodiments a Viterbialgorithm is utilized for the field of view control techniques thatimplement a sum-of-difference algorithm. In that regard, thesum-of-difference algorithm often produces multiple candidate markerlocations for every frame processed due to the fact that the width ofthe marker often spans multiple A-Scans within a frame. However, one ofthese peaks, and the corresponding A-scan, must be picked as thelocation of the marker. Several techniques can be used to pick the peak,such as the largest peak, the first peak greater than a threshold, andthe median peak of a plurality of candidate peaks. In some instances,these techniques are noisy (e.g., inconsistent from frame to frame)because they do not use any previous marker locations to help determinethe current marker location, resulting in marker locations that can varyconsiderably from frame to frame.

The Viterbi algorithm uses the past history of marker locations toreduce the noise in picking the marker location. More specifically, themarker location is picked by finding the largest value of a meritfunction. The merit function is computed by taking the values of thecandidate marker locations and adding a cost function that punishesjumps in the marker position. In some implementations, the Viterbialgorithm is implemented using one or more of the following steps:obtain the candidate A-scans (e.g., from the sum-of-differencealgorithm); input the candidate A-scans into a two-dimensional arraywhere the x direction represents time and the y direction is markerposition; update the two-dimensional array after each frame (or at aregular frame interval) so that a time history of marker locations andtheir relative positions is maintained; once the array is full with thedesired amount of time history (e.g., based on threshold timeframe,number of frames, and/or combinations thereof), perform the Viterbialgorithm.

In that regard, for a state space S, initial probabilities π_(k) ofbeing in state I and transition probabilities a_(i,j) of transitioningfrom state i to state j, where outputs y₁, . . . , y_(T) are observed,then the most likely state sequence x₁, . . . , x_(T) that produces theobservations is given by:V _(1,k) =P(y ₁ |k)·π_(k)V _(t,k) =P(y _(t) |k)·max_(xεS)(a _(x,k) ·V _(t−1,x))In that regard, V_(t,k) is the probability of the most probable statesequence responsible for the first t observation that has k as its finalstate. The Viterbi path is determined by saving past data that rememberthe state x that was used. Accordingly, letting Ptr(k,t) be the functionthat returns the value of x used to compute V_(t,k) if t>1 and k if t=1,thenx _(T)=argmax_(xεS)(V _(T,x))x _(t−1) =Ptr(x _(t)t )While these equations have been described, it is understood that anyimplementation of a Viterbi algorithm may be utilized as will beunderstood by those skilled in the art. Below, an exemplaryimplementation of the Viterbi algorithm in the context of the presentdisclosure is described. Starting with the oldest values in thetwo-dimensional array, the merit function is calculated for each columnof the array. In some instances, the merit function is a combination ofthe value of the peak and the cost function. For example, in someimplementations the merit function is constructed of two terms that areadded together for each potential peak location. In some embodiments,the first term is the value of a sum-of-difference calculationnormalized by the largest peak to an 8 bit value, and the second term isthe cost function that is described below. In that regard, the costfunction is chosen as a parabolic shaped function that has a maximumvalue of 0 at the peak and decreases in y in some implementations.Accordingly, the cost function may be defined by a 2^(nd) order equationof the form of X²+X. This produces a parabolic shaped cost function. Insome particular implementations, the cost function equation isimplemented as−X ²/10−2X.This particular function scales nicely for 8 bit data. As a result, insome implementations the cost function is a parabola that has a maximumvalue of zero and tails off to a minimum value of −255. This costfunction is applied to each line within the array and summed along thetime axis. After applying the cost function, the maximum value of themerit function is determined for the newest column in time. The position(e.g., A-scan) corresponding to the maximum value is selected as thelocation of the marker.

Further, in some instances at least a portion of the return path of thetransducer is included in a frame containing the forward path of thetransducer, which results in the marker appearing multiple times withina single frame. Techniques of the present disclosure described below canbe utilized to take this into account in determining marker location inconjunction with the field of view control algorithms described above.In some implementations it is desirable to focus on the forward scan.Accordingly, by placing a stronger emphasis on the earlier markerlocations in the frame, the end of the forward scan can be moreaccurately determined based on the marker location. In some instances, afirst peak bias is added to the field of view control algorithm. Forexample, in implementations incorporating a Viterbi algorithm the firstpeak bias is added as an additional term of the Viterbi cost functionthat biases the selection of the marker location to earlier peaks in theimage. In some instances, the early peak bias is a negatively slopedramp that starts at zero and linearly ramps down to the user setnegative value. In some implementations, the value of the peak bias isrepresented in 8 bits. In some embodiments, the slope of this linearterm is determined by finding the location of the first peak in asum-of-difference calculation and ramping down the bias to the end ofall the candidate peak locations. In some instances, the equation forthe bias isBias=(user defined value)/(last peak location−first peak location)*Ywhere Y is the position of the sum-of-difference peaks. This bias isadded on to the cost function, which has the effect to lower the maximumvalue of the cost function. Accordingly, in some instances the costfunction equation becomes −X²/10−2X+Bias. The amount of bias is set bythe user and can be used to construct a function that decreases in time,either linear, or polynomial. This function can then be applied to theset of potential marker locations acquired within a frame, therebybiasing the selected marker location towards the initial potentialmarker locations that correspond to the end of the forward scan. Thoseskilled in the art will recognize that if desired this approach can besimilarly utilized to identify later peaks, for example where the returnscan is of interest.

In another embodiment, a point of symmetry algorithm is used to find theturnaround point of the image to assist in selecting the markerlocation. In that regard, as the transducer is driven by the actuator itscans across the target in one direction. At some point the actuatorcannot continue to drive transducer in the original direction and thereturn mechanism starts moving the transducer in the opposite direction,imaging a region that the transducer has already swept through. Thisturnaround point is the point of symmetry. In some implementations, thispoint is found by computing the correlation coefficient between two submatrices that are extracted from the marker region of interest asdetermined by one of the field of view control algorithms describedabove. Generally, the sub matrices are adjacent to each other so thereis a right and left matrix. In some implementations, the right matrix isutilized as the template matrix and flipped across its left boundary ormirrored across its left boundary to define a mirror matrix (i.e., theleft matrix) and the cross correlation coefficient is computed betweenthe two matrices. In other implementations, the left matrix is utilizedas the template matrix and flipped across its right boundary or mirroredacross its right boundary to define a mirror matrix (i.e., the rightmatrix) and the cross correlation coefficient is computed between thetwo matrices. A high correlation value indicates high amount ofsymmetry. Thus, the highest correlation value is associated with thepoint of symmetry. This additional point of symmetry algorithm may beimplemented to avoid keying in on the mirror incident (e.g., as imagedon the return path) of the marker.

In some instances, the point of symmetry algorithm is implemented usingone or more of the following steps. Initially, a region of interest isidentified (e.g., using one or more the techniques describedpreviously). The region of interest will be utilized to identify thepoint of symmetry. The region of interest is selected to be a portion ofthe image that includes the marker. In some instances, the region ofinterest is the same region of interest that is used in thesum-of-differences calculation. Accordingly, the region of interest canbe identified using the same technique as in the sum-of-differencesmarker detection. In some instances, the region of interest is userselectable.

With the region of interest identified, the mean value for the rows ofthe image in the region of interest is calculated. In this context, arow represents a single image depth common across multiple A-scans. Inthat regard, a single A-scan includes image data for a range of depthsand each row is associated with a particular depth. In someimplementations, the region of interest extends across multiple rows.The mean value is then subtracted from each row in the region ofinterest to provide a value indicative of the difference relative to themean value for each row. Two adjacent sub-matrices are defined withinthe region of interest as described above. For example, as shown in FIG.47, a field of view having a starting orientation (represented by axis126) and an ending orientation (represented by axis 128) associated withmovement of an imaging transducer has a sub-matrix 800 and an adjacentsub-matrix 802 defined within the region of interest 804. The imagingtransducer generally travels between the starting orientation and theending orientation at an angle between about 1 degree and about 400degrees, depending on the imaging application. In some instances, theangle is between about 25 degrees and about 360 degrees. In someparticular instances, the angle is approximately 120 degrees.

In some instances, the sub-matrix 802 has a fixed angular size bydefault (i.e., the sub-matrix is comprised of the number of A-scansnecessary to cover a specific field of view angle). In that regard, thefixed angular size of the sub-matrix 802 is set in some instances basedon an angular width that matches the size of the marker to be detected.In order for the sub-matrices 800, 802 to match each other in the fieldof view angle represented, the sizes of sub-matrices 800, 802 vary dueto the dependence of the angular velocities on position. In that regard,in order to accurately compare the sub-matrices 800, 802, thesub-matrices should cover the same angle in the field of view. Becausethe angular velocity of the transducer on the forward scan may bedifferent than the angular velocity of the transducer on the return scan(i.e., faster or slower), the amount of time it takes the transducer totraverse a given angle may be correspondingly different between theforward and return scans and/or portions of each of the forward andreturn scans. For example, the transducer typically has a faster angularvelocity at the beginning of the forward scan than at the end of theforward scan. Accordingly, in some instances, one or both of thesub-matrices 800, 802 are sized to cover a specific field of view anglebased on the position of the sub-matrix in the field of view of thetransducer.

To this end, as has been described above, in some instances thetransducer is driven by an actuator to a turnaround point where theactuator stops and moves in the opposite direction where the returnmotion is controlled by a spring that has been stretched while theactuator was driving the transducer in the forward direction. Often, thereturn spring imparts a much more constant motion on the transducer thanthe actuator. As a result, the return motion has a more constant A-linedensity across multiple scans than the non-linear forward scanningmotion of the transducer. Therefore, in some implementations the size ofthe sub-matrix 802 is fixed based on the known, relatively constantreturn motion. On the other hand, the non-linear forward scanning of theactuator produces a lower line density in the beginning of the frame andhigher line density at the end of the forward scan due to the decreasingangular velocity of the transducer. Accordingly, the size of sub-matrix800 can be selected to cover the equivalent field of view angle ofsub-matrix 802, for example by up sampling or down sampling the numberof A-scans associated with sub-matrix 802. Accordingly, the resultingsizes of the sub-matrices 800, 802 to cover a common field of view anglemay consist of a different number of A-scans. In some instances, linearinterpolation/decimation is utilized to resize the sub-matrix 800 tomatch the size of the sub-matrix 802.

In some embodiments, the sub-matrices 800, 802 are defined by startingat the center angle of the region of interest and splitting the matrixencompassing the entire region of interest into the component templatesub matrix 802 and the mirror sub matrix 800. With the sub-matrices 800,802 defined and resized (as necessary) a correlation coefficient betweenthe two sub-matrices is calculated. In some implementations, thecorrelation coefficient is calculated using at least one of a Pearsonproduct-moment correlation coefficient, a Spearman's rank correlationcoefficient, a Kendall Tau rank correlation coefficient, and/or othersuitable correlation coefficient calculation technique. Further, in someinstances other techniques for evaluating the similarity between datasets are utilized, such as cross-correlation and/or other statisticalevaluation techniques.

After calculation of the correlation coefficient between sub-matrices800, 802, the axis/angle over which the template matrix is flipped isshifted to the right, for example by a fixed angle amount (e.g., 1degree, 2 degrees, etc.) or fixed time amount. The template matrix sizeis adjusted to match the desired field of view angle for the templatematrix based on the A-scan line density associated with the neworientation. Similarly, the size of the mirror matrix is adjusted asnecessary to match the new template matrix based on the new orientation.With the new sub-matrices defined and resized (as necessary) acorrelation coefficient between the two new sub-matrices is calculated.This iterative process (i.e., adjusting the location of the matricesacross the region of interest and calculating corresponding correlationcoefficients) is repeated across the entire region of interest. In thatregard, FIGS. 48-51 generally show aspects of this iterative process.More specifically, FIG. 48 shows the template matrix 802 defined withinthe region of interest of the field of view of the transducer to cover adesired angle within the field of view, which is based on the angularsize of the marker to be detected in some implementations. FIG. 49 showsthe mirror matrix 800 being defined by flipping or mirroring thetemplate matrix 802 over the left boundary of the template matrix 802.With the matrix 800 resized, as necessary, to match the desired field ofview angle of the template matrix 802, a correlation coefficient iscalculated between the matrices 800, 802. Subsequently, the location ofthe template matrix 802 is moved continuously or step-wise to the right(as shown in FIGS. 48-51) along the path of the transducer through theregion of interest. In that regard, FIG. 50 shows the template matrix802′ that is positioned at the far right boundary of the region ofinterest of the field of view. In other words, template matrix 802′represents the last iteration of the movement of the template matrix 802across the region of interest. FIG. 51 shows the mirror matrix 800′defined by flipping or mirroring the template matrix 802′ over the leftboundary of the template matrix 802′. With the matrix 800′ resized, asnecessary, to match the desired field of view angle of the templatematrix 802′, a correlation coefficient is calculated between thematrices 800, 802.

By finding the location in the region of interest of the image thatproduces the largest value of the correlation coefficient between thematrices 800, 802 as the matrices 800, 802 are moved across the regionof interest, the point of symmetry is identified. In someimplementations, a user supplied threshold is utilized to identify thepoint of symmetry. For example, in some instances the first locationthat results in a correlation coefficient meeting and/or exceeding thethreshold is identified as the point of symmetry. With the point ofsymmetry identified, selection of a marker location can be biased to aparticular appearance of the marker (e.g., the first occurring or thesecond occurring) in the image.

Utilizing the field-of-view control techniques described above, scanningmechanism performance can be adjusted in real time to account for deviceto device variation. In that regard, there are several parameters thatcontribute to the device to device variation, such as actuator shaftfriction, actuator return spring, pre-loading of transducer returnspring, transducer height and diameter and housing friction. Whileeffort is made to reduce the variations among these parameters duringmanufacturing and assembly, it is not possible to completely eliminatethe variation. As a result, a time consuming characterization step istypically necessary for every completed imaging device/system in orderto determine the scanning performance variation (e.g. scan time, scanvelocity) of that particular device/system. In addition to the time ittakes to characterize the device/system, the information specific tothat device/system must be stored, tracked, and used to run thatspecific device/system in the future. By utilizing one or more of themarkers and associated control techniques described above, the need forcharacterization and device/system specific information tracking can beeliminated or significantly reduced as at least some of the controltechniques themselves provide the necessary calibration of thedevice/system to ensure optimized imaging performance. In that regard,as described above, the feedback and control mechanisms of the presentdisclosure can adjust scanning parameters, such as actuator current oractuator current wave form, on the fly to compensate for variations. Asa result, any variation that exists between devices (or within a singledevice over time) is accounted for and adjusted for in real time as thedevice is used.

Further, the markers and control techniques of the present disclosureare also suitable for reducing image jitter. Image jitter is defined asthe variation in the scan angle vs. time between two consecutive framesor groups of consecutive frames. Jitter can be a result of slightchanges in the thermal environment or friction experienced duringconsecutive scans. These variations can be accounted for and compensatedfor in real time using the field-of-view control mechanisms of thepresent disclosure. In that regard, image jitter is also a function ofscan angle. For example, the last portions of a scan tend to suffer fromincreased image jitter as compared to the middle portions of the scan.Accordingly, one way to address this issue is to disregard the lastportions of a scan, such as the last 1, 5, 10, 15, 20, 25, 30, 35degrees, or a range defined by two of these values. By not displayingthe last portions of the scan, the most problematic jitter area iseliminated. Accordingly, by overdriving the scanning mechanism by theamount of scan angle that will not be displayed to the user, the fullfield of view is provided to the user without the jitter problemsassociated with the last portions of the scan. In that regard, thefield-of-view control mechanisms described above can be utilized tomonitor the amount of scan overdrive (or total rotational motion) andadjust as necessary to achieve the desired realized field-of-view takinginto consideration the portion that will be disregarded.

Although the present disclosure has been described primarily inconnection with the use of imaging transducers (e.g., devices suitablefor ultrasound imaging, optical coherence tomography imaging, and/orother scanning, oscillatory, and rotational imaging modalities), itshould be appreciated that the present disclosure can be utilized inother medical devices in which it is desired to provide diagnosticand/or therapeutic procedures utilizing rapid oscillatory motion.

Further, although illustrative embodiments have been shown anddescribed, a wide range of modification, change, and substitution iscontemplated in the foregoing disclosure and in some instances, somefeatures of the present disclosure may be employed without acorresponding use of the other features. It is understood that suchvariations may be made in the foregoing without departing from the scopeof the present disclosure. Accordingly, it is appropriate that theappended claims be construed broadly and in a manner consistent with thescope of the present disclosure.

What is claimed is:
 1. A method of controlling an intravascular imagingdevice, the method comprising: communicating, by a computer device incommunication with the intravascular imaging device, an initial controlsignal to an actuator of the intravascular imaging device to cause aninitial oscillation of an imaging element of the intravascular imagingdevice across a first field-of-view angle, wherein the intravascularimaging device further includes an imaging marker; receiving an initialimage from the imaging element of the intravascular device; identifyinga region of the initial image as containing image data of the imagingmarker; defining the region as a template representative of the imagingmarker; communicating, by the computer device in communication with theintravascular imaging device, a control signal to the actuator of theintravascular imaging device to cause oscillation of the imaging elementof the intravascular imaging device across a second field-of-view angledifferent than the first field-of-view angle; receiving imaging datafrom the imaging element of the intravascular imaging device;identifying the imaging marker in the imaging data by determining acorrelation between the imaging data and the template representative ofthe imaging marker; adjusting an aspect of the control signal based onidentifying the imaging marker; and communicating the adjusted controlsignal to the actuator of the intravascular imaging device.
 2. Themethod of claim 1, wherein identifying the imaging marker includesidentifying the imaging marker in a series of frames of the imagingdata.
 3. The method of claim 2, wherein identifying the imaging markerincludes determining a maximum correlation between the imaging data andthe template in the series of frames of the imaging data.
 4. The methodof claim 1, wherein adjusting said aspect of the control signal includesadjusting at least one of power or energy to the actuator to cause theactuator to move the imaging element such that the imaging marker ispositioned in a desired location in a subsequent frame of the imagingdata.
 5. The method of claim 1, wherein the imaging marker is located ata shallow depth of focus of the imaging element, and wherein the shallowdepth of focus is closer to the imaging element than a target depth offocus that contains an internal structure of a patient.
 6. The method ofclaim 5, wherein determining the correlation includes determining thecorrelation between the template and a region of interest located at theshallow depth of focus in the imaging data.
 7. The method of claim 6,wherein identifying the imaging marker includes identifying the imagingmarker in a series of frames of the imaging data, and wherein the regionof interest is selected based on at least one of: a distance of theimaging marker relative to the imaging element, an angular extent of theimaging marker relative to a field of view of the imaging element, amaximum anticipated frame-to-frame change in angle coverage, an imagingmarker location in a previous frame, and image depth, or a maximumexpected imaging element motion between frames.
 8. The method of claim1, wherein identifying the region of the initial image as containingimage data of the imaging marker comprises determining a location of theimaging marker within the initial image using a threshold algorithm or arunning average algorithm.
 9. A method of controlling an imaging device,the method comprising: communicating, by a computer device incommunication with the imaging device, an initial control signal to anactuator of the imaging device to cause an initial oscillation of animaging element of the imaging device across a first field of view wherean imaging marker of the imaging device is not located; receiving aninitial image of the first field of view from the imaging element of theintravascular device; defining the initial image as a template of aconstant signal region; receiving, by the computer device incommunication with the imaging device, imaging data representative of asecond field of view of the internal structure of a patient from theoscillating imaging element of the imaging device; processing theimaging data to identify the imaging marker in the imaging data, whereinprocessing the imaging data to identify the imaging marker includescorrelating the imaging data and the template of the constant signalregion, wherein a region of minimum correlation between the image dataand the template indicates a location of the imaging marker; andadjusting, based on processing the imaging data to identify the imagingmarker, a control signal provided to the actuator of the imaging deviceto achieve a desired field of view for the oscillating imaging element.10. The method of claim 9, wherein processing the imaging data includesidentifying the imaging marker in a series of frames of the imagingdata.
 11. The method of claim 10, wherein adjusting said aspect of thecontrol signal includes adjusting at least one of power or energyprovided to the actuator to cause the actuator to move the oscillatingimaging element such that the imaging marker is positioned in a desiredlocation in a subsequent frame of the imaging data.
 12. The method ofclaim 9, wherein the imaging marker is located at a shallow depth offocus of the imaging element, and wherein the shallow depth of focus iscloser to the imaging element than a target depth of focus that containsthe internal structure of the patient.
 13. The method of claim 12,wherein processing the imaging data includes correlating the templateand a region of interest located at the shallow depth of focus in theimaging data.
 14. The method of claim 13, wherein processing the imagingdata includes identifying the imaging marker in a series of frames ofthe imaging data, and wherein the region of interest is selected basedon at least one of: a distance of the imaging marker relative to theoscillating imaging element, an angular extent of the imaging markerrelative to the desired field of view of the oscillating imagingelement, a maximum anticipated frame-to-frame change in angle coverage,an imaging marker location in a previous frame, and image depth, or amaximum expected oscillating imaging element motion between frames. 15.An imaging system, comprising: an imaging device including: a flexibleelongate member having a proximal portion and a distal portion, theflexible elongate member sized and shaped for use within an internalstructure of a patient; an imaging element positioned at the distalportion of the flexible elongate member; an imaging marker positioned atthe distal portion of the flexible elongate member; and an actuatoroperable to impart oscillating motion to the imaging element; and acontroller in communication with the imaging device, the controlleroperable to: cause an initial oscillation of the imaging element acrossa first field-of-view angle; receive an initial image from the imagingelement; identify a region of the initial image as containing image dataof the imaging marker; define the region as a template representative ofthe imaging marker; receive imaging data of the internal structure ofthe patient across a second field-of-view angle different than the firstfield-of-view angle from the imaging element of the imaging device;identify the imaging marker in the imaging data by determining acorrelation between the imaging data and the template representative ofthe imaging marker; and adjust a control signal provided to the actuatorof the imaging device to achieve a desired field of view for the imagingelement based on identifying the imaging marker in the imaging data. 16.The system of claim 15, wherein the controller is operable to identifythe imaging marker in a series of frames of the imaging data.
 17. Thesystem of claim 16, wherein the controller is operable to identify theimaging marker in the imaging data by calculating a maximum correlationbetween the imaging data and the template representative of the imagingmarker in the series of frames of the imaging data.
 18. The system ofclaim 15, wherein the controller is operable to identify the region ofthe initial image as containing image data of the imaging marker usingat least one of a thresholding algorithm or a running average algorithm.